Search

Abstract Blockchain technology is a highly developed database system that shares information within a business web. It stores details in blocks connected chronologically, ensuring information integrity through consensus mechanisms that prevent unauthorised alterations. This decentralised system removes the need for a believable mediator, mitigating vulnerabilities and enhancing transaction security. Blockchain’s application spans the energy, finance, media, entertainment, and retail sectors. However, classical blockchain faces threats from quantum computing advancements, necessitating the development of quantum blockchain technology. Quantum blockchain, leveraging quantum computation and information theory, offers enhanced security and immutability. In this paper, different mathematical foundations, practical implementations and effectiveness of lattice‐based cryptography in securing blockchain applications are discussed. Analysis of how the cryptographic techniques can protect blockchain systems against quantum attacks is being done by using mathematical formulations and examples. Quantum computing strengthens blockchain security with advanced encryption and authentication, which is critical for safeguarding diverse sectors from evolving cyber threats. Further study on quantum‐resistant design is necessary if blockchain networks are to be robust and intact in the face of future technological developments.

Abstract Synthetic Aperture Radar (SAR) integrated with deep learning has been widely used in several military and civilian applications, such as border patrolling, to monitor and regulate the movement of people and goods across land, air, and maritime borders. Amongst these, maritime borders confront different threats and challenges. Therefore, SAR-based ship detection becomes essential for naval surveillance in marine traffic management, oil spill detection, illegal fishing, and maritime piracy. However, the model becomes insensitive to small ships due to the wide-scale variance and uneven distribution of ship sizes in SAR images. This increases the difficulties associated with ship recognition, which triggers several false alarms. To effectively address these difficulties, the present work proposes an ensemble model (eYOLO) based on YOLOv4 and YOLOv5. The model utilizes a weighted box fusion technique to fuse the outputs of YOLOv4 and YOLOv5. Also, a generalized intersection over union loss has been adopted in eYOLO which ensures the increased generalization capability of the model with reduced scale sensitivity. The model has been developed end-to-end, and its performance has been validated against other reported results using an open-source SAR-ship dataset. The obtained results authorize the effectiveness of eYOLO in multi-scale ship detection with an F 1 score and mAP of 91.49% and 92.00%, respectively. This highlights the efficacy of eYOLO in multi-scale ship detection using SAR imagery.

Abstract Air is an essential human necessity, and inhaling filthy air poses a significant health risk. One of the most severe hazards to people’s health is air pollution, and appropriate precautions should be taken to monitor and anticipate its quality in advance. Among all the countries, the air quality in India is decreasing daily, which is a matter of concern to the health department. Many studies use machine learning and Deep learning methods to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. Many research studies confuse researchers and readers about how to proceed with further research. This paper aims to give every detail of the considered air pollutants and brief about the techniques used, their advantages, and challenges faced during pollutant prediction, which leads to a better understanding of the techniques before starting any research related to air pollutant prediction. This paper has given numerous prospective questions on air pollution that piqued the study’s interest. This study discussed various machine and deep learning methods and optimization techniques. Despite all the discussed machine learning and deep learning techniques, the paper concluded that more datasets, better learning techniques, and a variety of suggestions would enhance interpretability while maintaining high accuracy for air pollution prediction. The purpose of this review is also to reveal how a family of neural network algorithms has helped researchers across the globe to predict air pollutant(s).

This study emphasizes a multi-pronged approach to improving the energy efficiency of Multi-Effect Evaporator (MEE) in the paper industry. By incorporating traditional Energy-Saving Schemes (ESSs) and innovative renewable energy sources, the study demonstrates significant potential for reducing energy consumption and environmental impact, making it a decisive pathway for industrial sustainability. Key ESS strategies include Thermo-Vapor Compressors, Feed Preheaters, and Steam- and Feed-Split, which are employed to enhance Steam Economy (SE) to evaluate MEE efficiency. This integration results in a 67.93% enhancement in SE, reducing energy consumption significantly. Further, SE enhancement is achieved by integrating flash tanks that capture and reuse excess heat, which boosts SE by an additional 5.89%, leading to a total improvement of 73% without additional energy consumption. A significant innovation in the study is the integration of Linear Fresnel Reflectors (LFRs) based solar collectors and turbine-based wind energy sources to power the MEE and reduce reliance on conventional energy. This hybrid system decreases energy dependence by 62% for the base MEE and 34% for the hybrid MEE. The results are validated by comparing them with existing studies, confirming the effectiveness of the proposed method and offering significant energy and environment savings.

Background: Platelet-rich plasma (PRP) helps in the treatment of various insertion tendinitis, and the recurrence rate will be low. In this study, we used intralesional injections of autologous PRP for the treatment of insertion tendinitis. Aims and Objectives: Assessment of pain relief following PRP treatment for insertion tendinitis and ascertaining the efficacy of PRP as a treatment option for insertion tendinitis. Materials and Methods: The study was done at Maharani Laxmi Bai Medical College, Jhansi, between January 2021 and June 2022, including 100 patients were applied for the treatment of PRP. We assessed patients before treatment and at three follow-up time points: 2, 4, and 6 months after the end of treatment. During each evaluation, three parameters were noted: Victorian Institute of Sports Assessment- Achilles (VISA-A) score, the Visual Analog Scale, and patient satisfaction according to the roles and Maudsley score. Results: This was a prospective trial by study design conducted on 100 patients, which included 44 patients with plantar fasciitis and 56 patients with tennis elbow. The response to PRP injection in patients with plantar fasciitis was assessed by the VISA-A score. The pre-injection VISA-A score of patients with plantar fasciitis was 56.82 (mean with SD of 18.015), which improved to 65.45 in the 2nd month (mean with SD −18.857), 4th month with 69.77 (mean with SD–19.823), and at 6 months 71.82 (mean with SD–20.829). Statistical analysis with an analysis of variance test gave a P=0.001. In our study, the patients had gradual sequential improvement in VISA-A score throughout the follow-up, with maximum improvement from the pre-injection score were observed in 2 months (65.45 with SD 18.85). Conclusion: Autologous PRP injection is a safe and useful modality of treatment in the treatment of insertion tendinitis.

Mobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and semi-automated tools by analyzing 180 primary studies. Techniques include topic modeling, collocation finding, association rule-based, aspect-based sentiment analysis, frequency-based, word vector-based, and hybrid approaches. The study compares various tools for analyzing mobile app reviews based on performance, scalability, and user-friendliness. Tools like KEFE, MERIT, DIVER, SAFER, SIRA, T-FEX, RE-BERT, and AOBTM outperformed baseline tools like IDEA and SAFE in identifying emerging issues and extracting relevant information. The study also discusses limitations such as manual intervention, linguistic complexities, scalability issues, and interpretability challenges in incorporating user feedback. Overall, this mapping study outlines the current state of feature extraction from app reviews, suggesting future research and innovation opportunities for extracting software requirements from mobile app reviews, thereby improving mobile app development.

As mobile applications proliferate and user feedback becomes abundant, the task of identifying and resolving conflicts among application features is crucial for delivering satisfactory user experiences. This research, motivated to align application development with user preferences, introduces a novel methodology that leverages advanced Natural Language Processing techniques. The paper showcases the use of sentiment analysis using RoBERTa, topic modeling with Non-negative matrix factorization (NMF), and semantic similarity measures from Sentence-BERT. These techniques enable the identification of contradictory sentiments, the discovery of latent topics representing application features, and the clustering of related feedback instances. The approach detects conflicts by analyzing sentiment distributions within semantically similar clusters, further enhanced by incorporating antonym detection and negation handling. It employs majority voting, weighted ranking based on rating scores, and frequency analysis of feature mentions to resolve conflicts, providing actionable insights for prioritizing requirements. Comprehensive evaluations on large-scale iOS App Store and Google Play Store datasets demonstrate the approach's effectiveness, outperforming baseline methods and existing techniques. The research improves mobile application development and user experiences by aligning features with user preferences and providing interpretable conflict resolution strategies, thereby introducing a novel approach to the field of mobile application development.

Breast cancer ranks as the second most prevalent cancer globally and is the most frequently diagnosed cancer among women; therefore, early, automated, and precise detection is essential. Most AI-based techniques for breast cancer detection are complex and have high computational costs. Hence, to overcome this challenge, we have presented the innovative LightweightUNet hybrid deep learning (DL) classifier for the accurate classification of breast cancer. The proposed model boasts a low computational cost due to its smaller number of layers in its architecture, and its adaptive nature stems from its use of depth-wise separable convolution. We have employed a multimodal approach to validate the model’s performance, using 13,000 images from two distinct modalities: mammogram imaging (MGI) and ultrasound imaging (USI). We collected the multimodal imaging datasets from seven different sources, including the benchmark datasets DDSM, MIAS, INbreast, BrEaST, BUSI, Thammasat, and HMSS. Since the datasets are from various sources, we have resized them to the uniform size of 256 × 256 pixels and normalized them using the Box-Cox transformation technique. Since the USI dataset is smaller, we have applied the StyleGAN3 model to generate 10,000 synthetic ultrasound images. In this work, we have performed two separate experiments: the first on a real dataset without augmentation and the second on a real + GAN-augmented dataset using our proposed method. During the experiments, we used a 5-fold cross-validation method, and our proposed model obtained good results on the real dataset (87.16% precision, 86.87% recall, 86.84% F1-score, and 86.87% accuracy) without adding any extra data. Similarly, the second experiment provides better performance on the real + GAN-augmented dataset (96.36% precision, 96.35% recall, 96.35% F1-score, and 96.35% accuracy). This multimodal approach, which utilizes LightweightUNet, enhances the performance by 9.20% in precision, 9.48% in recall, 9.51% in F1-score, and a 9.48% increase in accuracy on the combined dataset. The LightweightUNet model we proposed works very well thanks to a creative network design, adding fake images to the data, and a multimodal training method. These results show that the model has a lot of potential for use in clinical settings.

Lyme carditis is an uncommon but potentially fatal manifestation of early disseminated Lyme disease. Timely diagnosis poses a clinical challenge due to the highly variable and non-specific symptomatology that can be easily overlooked, as well as the limited availability of specific and non-invasive diagnostic tests for assessing cardiac involvement. While cardiac magnetic resonance (CMR) imaging is the standard imaging modality for diagnosing various etiologies of cardiomyopathy, its application in Lyme carditis remains understudied. In this study, we present two cases of CMR-proven Lyme carditis and provide a comprehensive review of the existing literature on the use of CMR in this condition.

In the domain of cybersecurity, cyber threats targeting network devices are very crucial. Because of the exponential growth of wireless devices, such as smartphones and portable devices, cyber risks are becoming increasingly frequent and common with the emergence of new types of threats. This makes the automatic and accurate detection of network-based intrusion very essential. In this work, we propose a network-based intrusion detection system utilizing the comprehensive feature engineering approach combined with boosting machine-learning (ML) models. A TCP/IP-based dataset with 25,192 data samples from different protocols has been utilized in our work. To improve the dataset, we used preprocessing methods such as label encoding, correlation analysis, custom label encoding, and iterative label encoding. To improve the model’s accuracy for prediction, we then used a unique feature engineering methodology that included novel feature scaling and random forest-based feature selection techniques. We used three conventional models (NB, LR, and SVC) and four boosting classifiers (CatBoostGBM, LightGBM, HistGradientBoosting, and XGBoost) for classification. The 10-fold cross-validation methods were employed to train each model. After an assessment using numerous metrics, the best-performing model emerged as XGBoost. With mean metric values of 99.54 ± 0.0007 for accuracy, 99.53 ± 0.0013 for precision, 99.54 ± 0.001 for recall, and an F1-score of 99.53 ± 0.0014, the XGBoost model produced the best performance overall. Additionally, we showed the ROC curve for evaluating the model, which demonstrated that all boosting classifiers obtained a perfect AUC value of one. Our suggested methodologies show effectiveness and accuracy in detecting network intrusions, setting the stage for the model to be used in real time. Our method provides a strong defensive measure against malicious intrusions into network infrastructures while cyber threats keep varying.

Knowledge-based authentication techniques remain one of the proven ways of maintaining confidentiality, ensuring integrity, and guaranteeing the availability of an information system. They employ what a user knows (Passwords or PINs) to authorize or grant access to an information system. While passwords employ a fixed combination of characters, Personal Identification Numbers (PINs) are majorly numbers. Existing implementations of these authentication techniques involve the repetitive use of static passwords and PINs at every login instance. These have been exposed to various attacks, such as keyloggers, shoulder surfing, brute force, and dictionary attacks. To overcome these attacks, this study presents an authentication technique where users’ PINs are incremented during successive login attempts. Users are expected to choose a preferred incremental factor, which can be any number they can remember, that will be added to the default 6-digit PIN to produce a dynamic PIN that can be used in subsequent login sessions. Furthermore, an additional layer of security that involves the use of a dynamic 4 by 4 graphical grid was integrated into the proposed incremented PIN technique. At every login session, users are presented with a set of 16 possible PINs to choose from. The security analysis of the proposed authentication technique revealed that the proposed technique could resist existing password attacks, thereby enhancing security. A performance testing and usability analysis was also carried out among 1145 individuals who interacted with the web application that uses the incremental authentication technique. The questionnaire items were structured based on the constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model. Statistical analysis of the responses received showed an appreciable level of acceptance in terms of performance expectancy, effort expectancy, social influence, and facilitating conditions. The positive user acceptance results provide reassurance about the practicality and effectiveness of the proposed technique. It is believed that the proposed incremental graphical grid authentication technique will further enhance the security of our growing mobile and web applications.

Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced explanation techniques with engaging visualizations to address this issue. Our system integrates a bidirectional Long Short-Term Memory (BiLSTM) model with attention mechanisms, enhanced by Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). We evaluate this approach on a diverse dataset of 150,000 app reviews, achieving an F1 score of 0.82 and 89% accuracy, significantly outperforming baseline Support Vector Machine (F1: 0.66) and Convolutional Neural Network (CNN) (F1: 0.72) models. Our empirical user studies with developers demonstrate that our explainable approach improves trust (27%) when explanations are provided and correct interpretation (73%). The system’s interactive visualizations allowed developers to validate predictions, with over 80% overlap between model-highlighted phrases and human annotations for feature requests. These findings highlight the importance of integrating explainable AI into real-world software engineering workflows. The paper’s results and future directions provide a promising approach for feature request detection in app reviews to create more transparent, trustworthy, and effective AI systems.

The most widely used asymmetric cipher is ECC. It can be applied to IoT applications to offer various security services. However, a wide range of sectors have been investigated for applying ECC. The field of elliptic curve cryptographic processors for GF (2191) has received less attention. This study presents a low-resource, high-efficiency architecture for a 191-bit ECC processor. This design uses a novel hybrid Karatsuba multiplier for the multiplication of finite fields. For GF (2191), the Quad-Itoh-Tsuji algorithm has been altered to provide a small-size inversion unit. PlanAhead software synthesizes the CPU, which is then implemented on several Xilinx FPGAs. With savings in slice consumption ranging from 16 to 43 percent, the implemented design is the most restricted compared to the current designs. Compared to previously published designs, it is 3.8–1000 times faster. The elliptic curve scalar multiplication on the Virtex-7 FPGA is computed in $7.24~\mu $ s. Additionally, the proposed design achieves savings in area-time products of 77 to 90 percent. It may be beneficial for IoT edge devices. It utilizes 3120 mW of power for the operation. A state-of-the-art comparison based on the figure of merit (FoM) reveals that the proposed design outclasses the newest designs by a large margin. It also exhibits a throughput of 138.121 Kbps.

Background: Dysregulation of receptor tyrosine kinase c-MET is known to promote tumor development by stimulating oncogenic signaling pathways in different cancers, including pediatric neuroblastoma (NB). NB is an extracranial solid pediatric cancer that accounts for almost 15% of all pediatric cancer-related deaths, with less than a 50% long-term survival rate. Results: In this study, we analyzed a large cohort of primary NB patient data and revealed that high MET expression strongly correlates with poor overall survival, disease progression, relapse, and high MYCN levels in NB patients. To determine the effects of c-MET in NB, we repurposed a small molecule inhibitor, tivantinib, and found that c-MET inhibition significantly inhibits NB cellular growth. Tivantinib significantly blocks NB cell proliferation and 3D spheroid tumor formation and growth in different MYCN-amplified and MYCN-non-amplified NB cell lines. Furthermore, tivantinib blocks the cell cycle at the G2/M phase transition and induces apoptosis in different NB cell lines. As expected, c-MET inhibition by tivantinib inhibits the expression of multiple genes in PI3K, STAT, and Ras cell signaling pathways. Conclusions: Overall, our data indicate that c-MET directly regulates NB growth and 3D spheroid growth, and c-MET inhibition by tivantinib may be an effective therapeutic approach for high-risk NB. Further developing c-MET targeted therapeutic approaches and combining them with current therapies may pave the way for effectively translating novel therapies for NB and other c-MET-driven cancers.

The classification of ECG signals is a critical process because it guides the diagnosis of the proper treatment process for the patient. However, any form of disturbance with ECG signals can be highly conspicuous because of the mechanics involved in data acquisition from living beings, which has a significant impact on the classification procedure. The purpose of this research work is to advance ECG signal classification results by employing numerous denoising methods and, in turn, boost the accuracy of cardiovascular diagnoses. To simulate realistic conditions, we added various types of noise to ECG data, including Gaussian, salt and pepper, speckle, uniform, and exponential noise. To overcome the interference of noise from environments in the obtained ECG signals, we employed wavelet transform, median filter, Gaussian filter, and the hybrid of the wavelet and median filters. The proposed hybrid denoising method has better results than the other methods because of the use of wavelet multi-scale analysis and the ability of the median filter to avoid the loss of vital ECG characteristics. Thus, despite a certain proximity in the values, the hybrid method is significantly more accurate and reliable, as evidenced by the mean squared error (MSE), mean absolute error (MAE), R-squared, and Pearson correlation coefficient. More specifically, the hybrid approach provided an MSE of 0.0012 and an MAE of 0.025, the R-squared value for this study was 0.98, and the Pearson correlation coefficient was 0.99, which provides a very good resemblance to the original ECG confirmation. The proposed classification model is based on the modified lightweight CNN or MLCNN that was trained using the noisy and the denoised data. The findings demonstrated that by applying the denoised data, the testing accuracy, precision, recall, and F1 scores achieved 0.92, 0.91, 0.90, and 0.91 for the datasets, while the noisy data achieved 0.80, 0.78, 0.82, and 0.80, respectively. In this study, the signal quality and denoising methods were found to enhance ECG signal classification and diagnostic accuracy while encouraging proper preprocessing in future studies and applications for real-time ECG for cardiac care.

Machine learning, a rapidly growing field, has attracted numerous researchers for its ability to automatically learn from and make predictions based on data. This manuscript presents an innovative approach to estimating the covariance matrix of noise in radar measurements for target tracking, resulting from collaborative efforts. Traditionally, researchers have assumed that the covariance matrix of noise in sonar measurements is present in the vast majority of literature related to target tracking. On the other hand, this research aims to estimate it by employing deep learning algorithms with noisy measurements in range, bearing, and elevation from radar sensors. This collaborative approach, involving multiple disciplines, provides a more precise and accurate covariance matrix estimate. Additionally, the unscented Kalman filter was combined with the gated recurrent unit, multilayer perceptron, convolutional neural network, and long short-term memory to accomplish the task of 3D target tracking in an airborne environment. The quantification of the results was achieved through the use of Monte Carlo simulations, which demonstrated that the convolutional neural network performed better than any other approach. The system was simulated using a Python program, and the proposed method offers higher accuracy and faster convergence time than conventional target tracking methods. This is a demonstration of the potential that collaboration can have in research.

Time series forecasting across different domains has received massive attention as it eases intelligent decision-making activities. Recurrent neural networks and various deep learning algorithms have been applied to modeling and forecasting multivariate time series data. Due to intricate non-linear patterns and significant variations in the randomness of characteristics across various categories of real-world time series data, achieving effectiveness and robustness simultaneously poses a considerable challenge for specific deep-learning models. We have proposed a novel prediction framework with a multi-phase feature selection technique, a long short-term memory-based autoencoder, and a temporal convolution-based autoencoder to fill this gap. The multi-phase feature selection is applied to retrieve the optimal feature selection and optimal lag window length for different features. Moreover, the customized stacked autoencoder strategy is employed in the model. The first autoencoder is used to resolve the random weight initialization problem. Additionally, the second autoencoder models the temporal relation between non-linear correlated features with convolution networks and recurrent neural networks.Finally, the model's ability to generalize, predict accurately, and perform effectively is validated through experimentation with three distinct real-world time series datasets. In this study, we conducted experiments on three real-world datasets: Energy Appliances, Beijing PM2.5 Concentration, and Solar Radiation. The Energy Appliances dataset consists of 29 attributes with a training size of 15,464 instances and a testing size of 4239 instances. For the Beijing PM2.5 Concentration dataset, there are 18 attributes, with 34,952 instances in the training set and 8760 instances in the testing set. The Solar Radiation dataset comprises 11 attributes, with 22,857 instances in the training set and 9797 instances in the testing set. The experimental setup involved evaluating the performance of forecasting models using two distinct error measures: root mean square error and mean absolute error. To ensure robust evaluation, the errors were calculated at the identical scale of the data. The results of the experiments demonstrate the superiority of the proposed model compared to existing models, as evidenced by significant advantages in various metrics such as mean squared error and mean absolute error. For PM2.5 air quality data, the proposed model's mean absolute error is 7.51 over 12.45, about ∼40% improvement. Similarly, the mean square error for the dataset is improved from 23.75 to 11.62, which is ∼51%of improvement. For the solar radiation dataset, the proposed model resulted in ∼34.7% improvement in means squared error and ∼75% in mean absolute error. The recommended framework demonstrates outstanding capabilities in generalization and outperforms datasets spanning multiple indigenous domains.

Prolotherapy is one of the injection-based alternative therapeutic options for the treatment of chronic musculoskeletal pain. However, for chronic musculoskeletal conditions, corticosteroid and local anesthetic preparation like Lignocaine is widespread and initial injection therapy of choice. There are several other techniques also such as autologous whole blood, platelet-rich plasma, bone marrow, dry needling, and extracorporeal wave therapy. In prolotherapy, a very small amount of irritant or sclerosing solution is injected into joint space, ligament, or tendon insertion sites as a complementary medical treatment over several treatment seasons. Hyperosmolar dextrose, phenol–glycerine–glucose, and morrhuate sodium are the three most commonly used prolotherapy solutions. There are several studies and evidence available regarding the efficacy of prolotherapy over various musculoskeletal conditions such as chronic low back pain, osteoarthritis of joints, subluxation and joint instability, ligament laxity, hypermobility of joints, and tendinopathy. The efficacy of prolotherapy for different musculoskeletal conditions with different injections is different, and the precise mechanism of prolotherapy effect is still not clear, so further studies and clinical trials are recommended.

Lateral ankle sprain (LAS) is considered “an acute traumatic injury to the lateral ligament complex of the ankle joint as a result of excessive inversion of the rear foot or a combined plantar flexion and adduction of the foot”. Chronic ankle instability (CAI) is characterized by a history of one significant LAS, subsequent recurrent sprains, episodes of the ankle giving way, or self-reported deficits in ankle function for >1 year following the initial sprain. If untreated, instability may lead to cartilage deterioration and end in degenerative ankle disease. The objective of this review is to provide recent scientific evidence on CAI. Through this article, we have tried to put some light upon a problem that causes significant morbidity to the patient by compilation of the available evidence-based concepts. The need for this article is as there is the absence of a generalized treatment algorithm, the presence of various treatment modalities, the presence of two schools of opinion regarding treatment, and the absence of comprehensive text. Years of research have identified numerous impairments associated with CAI such as decreases in range of motion, strength, postural control, and altered movement patterns during functional activities when compared to individuals with no LAS history. As a result, an impairment-based rehabilitation model was developed to treat the common impairments associated with CAI. The impairment-based rehabilitation model has been shown to be an effective rehabilitation strategy at improving both clinical and patient-oriented outcomes in patients with CAI. We have reviewed the current literature regarding diagnosis, imaging, conservative methods, surgical approaches, and rehabilitation protocols.

Micro-Electro-Mechanical System (MEMS) switches have emerged as pivotal components in the realm of miniature electronic devices, promising unprecedented advancements in size, power consumption, and versatility. This literature review paper meticulously examines the key issues and challenges encountered in the development and application of MEMS switches. The comprehensive survey encompasses critical aspects such as material selection, fabrication intricacies, performance metrics including switching time and reliability, and the impact of these switches on diverse technological domains. The review critically analyzes the influence of design parameters, actuation mechanisms, and material properties on the performance of MEMS switches. Additionally, it explores recent advancements, breakthroughs, and innovative solutions proposed by researchers to address these challenges. The synthesis of the existing literature not only elucidates the current state of MEMS switch technology but also paves the way for future research avenues. The findings presented herein serve as a valuable resource for researchers, engineers, and technologists engaged in advancing MEMS switch technology, offering insights into the current landscape and guiding future endeavors in this rapidly evolving field.

The Raf-MEK-ERK signaling network has been the subject of intense research due to its role in the development of human cancers, including pediatric neuroblastoma (NB). MEK and ERK are the central components of this signaling pathway and are attractive targets for cancer therapy. Approximately 3–5% of the primary NB samples and about 80% of relapsed samples contain mutations in the Raf-MEK-ERK pathway. In the present study, we analyzed the NB patient datasets and revealed that high RAF and MEK expression leads to poor overall survival and directly correlates with cancer progression and relapse. Further, we repurposed a specific small-molecule MEK inhibitor CI-1040 to inhibit the Raf-MEK-ERK pathway in NB. Our results show that CI-1040 potently inhibits NB cell proliferation and clonogenic growth in a dose-dependent manner. Inhibition of the Raf-MEK-ERK pathway by CI-1040 significantly enhances apoptosis, blocks cell cycle progression at the S phase, inhibits expression of the cell cycle-related genes, and significantly inhibits phosphorylation and activation of the ERK1/2 protein. Furthermore, CI-1040 significantly inhibits tumor growth in different NB 3D spheroidal tumor models in a dose-dependent manner and by directly inhibiting spheroidal tumor cells. Overall, our findings highlight that direct inhibition of the Raf-MEK-ERK pathway is a novel therapeutic approach for NB, and further developing repurposing strategies using CI-1040 is a clinically tractable strategy for effectively treating NB.

Recent advances in technology have changed our lives dramatically, in many ways for the better. Increasingly, people are using their electronic mobile devices to stay connected to the digital world. However, overuse of handheld devices can lead to unintended problems involving the hand, wrist, and arm. The article summarizes and reviews the literature with the musculoskeletal problems and adverse effects that can occur by excessive and inadvertent use of a smart phone.

Quercetin (QCT) is a naturally occurring phenolic flavonoid compound with inbuilt characteristics of antioxidant, anti-inflammatory, and immune protection. Several recent studies have shown that QCT and QCTits nanoparticles have therapeutic potential against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Novel therapeutics also include the implication of extracellular vesicles (EVs) to protect from SARS-CoV-2 viral infection. This article highlighted the therapeutic/prophylactic potential of engineered EVs loaded with QCT against SARS-CoV-2 infection. Several biotechnological engineering approaches are available to deliver EVs loaded with QCT nanoparticles. Among these biotechnological advances, a specific approach with significantly higher efficiency and yield has to be opted to fabricate such drug delivery of nano molecules, especially to combat SARS-CoV-2 infection. The current treatment regime protects the human body from virus infection but has some limitations including drugs and long-term steroid side effects. However, the vaccine strategy is somehow effective in inhibiting the spread of coronavirus disease-19 (COVID-19) infection. Moreover, the proposed exosomal therapy met the current need to repair the damaged tissue along with inhibition of COVID-19-associated complications at the tissue level. These scientific findings expand the possibilities and predictability of developing a novel and cost-effective therapeutic approach that combines the dual molecule, EVs and QCT nanoparticles, to treat SARS-CoV-2 infection. Therefore, the most suitable engineering method to fabricate such a drug delivery system should be better understood before developing novel therapeutics for clinical purposes.

Recently, the spread of fake images on social media platforms has become a significant concern for individuals, organizations, and governments. These images are often created using sophisticated techniques to spread misinformation, influence public opinion, and threaten national security. This paper begins by defining fake images and their potential impact on society, including the spread of misinformation and the erosion of trust in digital media. This paper also examines the different types of fake images and their challenges for detection. We then review the recent approaches proposed for detecting fake images, including digital forensics, machine learning, and deep learning. These approaches are evaluated in terms of their strengths and limitations, highlighting the need for further research. This paper also highlights the need for multimodal approaches that combine multiple sources of information, such as text, images, and videos. Furthermore, we present an overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection. This paper concludes by discussing future directions for fake image detection research, such as developing more robust and explainable methods, cross-modal fake detection, and the integration of social context. It also emphasizes the need for interdisciplinary research that combines computer science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake images. This survey paper will be a valuable resource for researchers and practitioners working on fake image detection on social media platforms.

Digital images cannot be excluded as part of a popular choice of information representation. Covert information can be easily hidden using images. Several schemes are available to hide covert information and are known as steganography schemes. Steganalysis schemes are applied on stego-images to assess the strength of steganography schemes. In this paper, a new steganalysis scheme is presented to detect stego-images. Predefined kernels guide the set of a conventional convolutional layer, and the tight cohesion provides completely guided training. The learning rate of convolutional layers with predefined kernels is higher than the global learning rate. The higher learning rate of the convolutional layers with predefined kernels assures adaptability according to network training, while still maintaining the basic attributes of high-pass kernels. The Leaky ReLU layer is exploited against the ReLU layer to boost the detection performance. Transfer learning is applied to enhance detection performance. The deep network weights are initialized using the weights of the trained network from high-payload stego-images. The strength of the proposed scheme is verified on the HILL, Mi-POD, S-UNIWARD, and WOW content-adaptive steganography schemes. The results are overwhelming and better than the existing steganalysis schemes.

Abstract Background Messenger RNA (mRNA) coronavirus disease of 2019 (COVID-19) vaccine are known to cause minor side effects at the injection site and mild global systemic symptoms in first 24–48 h. Recently published case series have reported a possible association between acute myocarditis and COVID-19 vaccination, predominantly in young males. Methods We report a case series of 5 young male patients with cardiovascular magnetic resonance (CMR)-confirmed acute myocarditis within 72 h after receiving a dose of an mRNA-based COVID-19 vaccine. Results Our case series suggests that myocarditis in this setting is characterized by myocardial edema and late gadolinium enhancement in the lateral wall of the left ventricular (LV) myocardium, reduced global LV longitudinal strain, and preserved LV ejection fraction. All patients in our series remained clinically stable during a relatively short inpatient hospital stay. Conclusions In conjunction with other recently published case series and national vaccine safety surveillance data, this case series suggests a possible association between acute myocarditis and COVID-19 vaccination in young males and highlights a potential pattern in accompanying CMR abnormalities.

IntroductionExtracellular vesicles (EVs) are known to have a significant role in the central nervous system (CNS) and neurodegenerative disease.MethodsPubMed, Scopus, ISI Web of Science, EMBASE, and Google Scholar were used to identify published articles about EV modifications (2012 to Feb 2022).ResultsIn total, 1,435 published papers were identified among the searched articles, with 1,128 non-duplicate publications being identified. Following the screening of titles and abstracts, 214 publications were excluded; following the full-text screening of 93 published articles, another 33 publications were excluded. The remaining 60 studies were considered. The kappa statistic of 0.868 indicated that the raters were highly reliable. Furthermore, the inter-reliability and intra-reliability coefficients were found to be 0.931 and 0.908, respectively, indicating strong reliability and consistency between the eligible studies identified by the raters. A total of 27 relevant studies demonstrated the role of EVs as therapeutic and diagnostic biomarkers in neurodegenerative diseases. Of note, 19 and 14 studies, respectively, found EVs to be pioneering in diagnostic and therapeutic roles.DiscussionEVs play an important role in the central nervous system (CNS), aiding in cell-to-cell communication and serving as a diagnostic marker and therapeutic target in a variety of neurodegenerative diseases. EVs are the home of several proteins [including-synuclein (-syn) and tau proteins], lipids, and genetic materials such as DNA and RNA. The presence of novel miRNAs in EVs suggests biomarkers for the diagnosis and screening of neurodegenerative disorders. Furthermore, EVs play an important role in the pathogenesis of such disorders. This systematic review discussed the current state of EVs’ role in neurological diseases, as well as some preclinical studies on the therapeutic and diagnostic potential of EVs.

BackgroundPeripheral nerve injury is a serious concern that leads to loss of neuronal communication that impairs the quality of life and, in adverse conditions, causes permanent disability. The limited availability of autografts with associated demerits shifts the paradigm of researchers to use biomaterials as an alternative treatment approach to recover nerve damage.PurposeThe purpose of this study is to explore the role of biomaterials in translational treatment approaches in diabetic neuropathy.Study designThe present study is a prospective review study.MethodsPublished literature on the role of biomaterials in therapeutics was searched for.ResultsBiomaterials can be implemented with desired characteristics to overcome the problem of nerve regeneration. Biomaterials can be further exploited in the treatment of nerve damage especially associated with PDN. These can be modified, customized, and engineered as scaffolds with the potential of mimicking the extracellular matrix of nerve tissue along with axonal regeneration. Due to their beneficial biological deeds, they can expedite tissue repair and serve as carriers of cellular and pharmacological treatments. Therefore, the emerging research area of biomaterials-mediated treatment of nerve damage provides opportunities to explore them as translational biomedical treatment approaches.ConclusionsPre-clinical and clinical trials in this direction are needed to establish the effective role of several biomaterials in the treatment of other human diseases.

Purpose: To evaluate the presence of SARS-CoV-2 RNA in the conjunctival swab sample of positive confirmed COVID-19 patients and to find out its association with comorbidity and severity of COVID-19 disease. Methods: We conducted an observational cross-sectional study at a dedicated tertiary COVID-19 hospital in central India for a period of 8 weeks from February 2021to March 2021. We included patients who tested positive for SARS-CoV-2 RNA through nasopharyngeal swab and were above 18 years of age. Swab samples have been collected within 48 h of admission. Conjunctival swab was taken from the lower fornix of both eyes and sent to microbiology laboratory for real-time- polymerase chain reaction (RT-PCR). Results: Out of 150 patients, conjunctival swab RT-PCR was positive in five patients (3.33%). Two patients had conjunctival manifestations in the form of conjunctivitis but conjunctival swab RT-PCR was negative in those patients. Among the RT-PCR positive patients, two (40%) were from mild, one (20%) was from moderate, and two (40%) were from severe category. No association could be established between conjunctival swab RT-PCR positivity and severity of the disease or associated comorbidity. Conclusion: Our study provides evidence that SARS-CoV-2 RNA could be detected in conjunctival secretions, and though the risk is relatively low, the eye may act as source of transmission. Extra caution should be taken by healthcare workers, and use of proper precautions like face shields and goggles should be encouraged.

Objective: It has been explored that Vitamin D play role in various non-skeletal disorders including Diabetes Mellitus. The present study was designed with the aim to assess association among control, pre-diabetic and diabetic with vitamin D and association between lipid profile and vitamin D. Materials and Methods: A total of 109 subjects were recruited for the cross-sectional study including 37 as control, 41 pre-diabetic and 31 diabetic. A clinical examination was done for all the groups including fasting samples (12hrs) for lipid parameters, serum 25 (OH) vitamin D level and (HbA1/C). Results: It was found that in control subjects 37.9% have the sufficient vitamin D3 level whereas 17.1% subjects in pre-diabetic, 16.6% in diabetic with good glycemic control and no subject was found to have sufficient vitamin D3 level in diabetic with poor glycemic control. The mean vitamin D3 levels was highest in control i.e. -26.53±11.99 ng/ml followed by 20.23±4.12ng/ml in pre-diabetics,19.07±8.01ng/ml in diabetics with good glycemic control and 12.92±6.77ng/ml in diabetics with poor glycemic control. HBA1/c and serum vitamin D3level share a significant association (P-value< 0.01).Total cholesterol (P-value< 0.0), serum triglyceride (P-value< 0.01), serum LDL cholesterol (P-value< 0.01) and serum VLDL (P-value< 0.01) had inverse association with vitamin D levels. HDL cholesterol has no effect with vitamin D. Conclusion: The present study showed vitamin D3 deficiency as a risk factor for worsening glycemic control and dyslipidemia.

BackgroundLung cancer screening for current or former heavy smokers is now recommended among all asymptomatic adults 50–80 years old with a 20 pack-year history of smoking. However, little is known about the smoking-related attitudes of this population.MethodAn assessment was conducted among 1,472 current smokers who presented for an annual lung cancer screen at one of 12 diagnostic imaging sites in Rhode Island between April 2019 and May 2020. Patients were asked about their use of smoking products, interest in quitting, and smoking-related attitudes.ResultsPatients smoked a median of 16 cigarettes per day; 86.6% were daily cigarette smokers and 30.1% were daily cigar smokers. In total, 91.4% of patients were, to some degree, interested in quitting smoking and 71.4% were seriously thinking about quitting in the next 6 months or sooner. Patients planned on smoking less regardless of whether their lung screen was positive or negative for cancer, though they were more likely to plan on smoking less if negative (on 0–3 pt Likert scale: 0.31, 95% CI [0.27, 0.34] vs. 0.77, 95% CI [0.72, 0.81]). Confidence in quitting and belief in one’s inherent ability to quit smoking varied substantially within the sample.ConclusionNearly all current smokers receiving a lung cancer screen have some interest in smoking cessation. Due to the heterogeneity in some smoking-related attitudes, tailored interventions for this population should be tested.

High-risk neuroblastoma (NB) represents a major clinical challenge in pediatric oncology due to relapse of metastatic, drug-resistant disease, and treatment-related toxicities. An analysis of 1235 primary NB patient dataset revealed significant increase in AKT1 and AKT2 gene expression with cancer stage progression. Additionally, Both AKT1 and AKT2 expression inversely correlate with poor overall survival of NB patients. AKT1 and AKT2 genes code for AKT that drive a major oncogenic cell signaling pathway known in many cancers, including NB. To inhibit AKT pathway, we repurposed an antiviral inhibitor BX-795 that inhibits PDK1, an upstream activator of AKT. BX-795 potently inhibits NB cell proliferation and colony growth in a dose-dependent manner. BX-795 significantly enhances apoptosis and blocks cell cycle progression at mitosis phase in NB. Additionally, BX-795 potently inhibits tumor formation and growth in a NB spheroid tumor model. We further tested dual therapeutic approaches by combining BX-795 with either doxorubicin or crizotinib and found synergistic and significant inhibition of NB growth, in contrast to either drug alone. Overall, our data demonstrate that BX-795 inhibits AKT pathway to inhibit NB growth, and combining BX-795 with current therapies is an effective and clinically tractable therapeutic approach for NB.

Background: Non-alcoholic fatty liver disease (NAFLD) also referred as metabolic as metabolic (dysfunction) associated fatty liver disease. Type 2 diabetes mellitus (T2DM) is a major cause in progression of NAFLD and non-alcoholic steatohepatitis (NASH). The aim of the present study is to assess the activity of liver enzymes in T2DM in North Indian population. Method: This was a cross-sectional descriptive study clinic-based study in patients with T2DM. A total of 612 participants (226 healthy controls and 386 T2DM) were recruited. Body mass index (BMI), activity of liver enzymes including alanine and aspartate aminotransferase (ALT, AST) along with alkaline phosphatase (ALP) was measured. Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) along with total protein (TP) and albumin were also measured. Quantitative variables were expressed as mean ± SD, while qualitative variables as frequencies (%). Pearson/Spearman correlation test, unpaired t-test, Chi-squared test was used to assess the correlation, association and significant differences between study groups respectively. A P-value of

SARS-CoV-2–infected patients are reported to show immunocompromised behavior that gives rise to a wide variety of complications due to impaired innate immune response, cytokine storm, and thrombo-inflammation. Prolonged use of steroids, diabetes mellitus, and diabetic ketoacidosis (DKA) are some of the factors responsible for the growth of Mucorales in such immunocompromised patients and, thus, can lead to a life-threatening condition referred to as mucormycosis. Therefore, an early diagnosis and cell-based management cosis is the need of the hour to help affected patients overcome this severe condition. In addition, extended exposure to antifungal drugs/therapeutics is found to initiate hormonal and neurological complications. More recently, mesenchymal stem cells (MSCs) have been used to exhibit immunomodulatory function and proven to be beneficial in a clinical cell-based regenerative approach. The immunomodulation ability of MSCs in mucormycosis patient boosts the immunity by the release of chemotactic proteins. MSC-based therapy in mucormycosis along with the combination of short-term antifungal drugs can be utilized as a prospective approach for mucormycosis treatment with promising outcomes. However, preclinical and in mucormyIn mucormycosis, the hyphae of clinical trials are needed to establish the precise mechanism of MSCs in mucormycosis treatment.

Digital images are very popular and commonly used for hiding crucial data. In a few instances, image steganography is misused for communicating with improper data. In this paper, a robust deep neural network is proposed for the identification of content-adaptive image steganography schemes. Multiple novel strategies are applied to improve detection performance. Two non-trainable convolutional layers is used to guide the proposed CNN with fixed kernels. Thirty-one kernels are used in both non-trainable layers, of which thirty are high-pass kernels and one is the neutral kernel. The layer-specific learning rate is applied for each layer. ReLU with customized thresholding is applied to achieve better performance. In the proposed method, image down-sampling is not performed; only the global average pooling layer is considered in the last part of the network. The experimental results are verified on BOWS2 and BOSSBase image sets. Content-adaptive steganography schemes, such as HILL, Mi-POD, S-UNIWARD, and WOW, are considered for generating the stego images with different payloads. In experimental analysis, the proposed scheme is compared with some of the latest schemes, where the proposed scheme outperforms other state-of-the-art techniques in the most cases.

Image steganography is applied to hide some secret information. Occasionally, steganography is used for malicious purposes to hide inappropriate information. In this paper, a new deep neural network was proposed to detect context-aware steganography techniques. In the proposed scheme, a high-boost filter was applied to alleviate the high-frequency while retaining the low-frequency details. The high-boost image was processed by thirty SRM high-pass filters to obtain thirty high-boost SRM filtered images. In the proposed CNN, two skip connections were used to collect information from multiple connections simultaneously. A clipped ReLU layer was considered in spite of the general ReLU layer. In constructing the CNN, a bottleneck approach was followed for an effective convolution. Only a single global average pooling layer was used to retain the complete flow of information. SVM was utilized instead of the softmax classifier to improve the detection accuracy. In the experimental results, the proposed technique was better than the existing techniques in terms of the detection accuracy and computational cost. The proposed scheme was verified on BOWS2 and BOSSBase datasets for the HILL, S-UNIWARD, and WOW context-aware steganography algorithms.

Pulmonary arterial hypertension (PAH) remains life-limiting despite numerous approved vasodilator therapies. Right ventricular (RV) function determines outcome in PAH but no treatments directly target RV adaptation. PAH is more common in women, yet women have better RV function and survival as compared to men with PAH. Lower levels of the adrenal steroid dehydroepiandrosterone (DHEA) and its sulfate ester are associated with more severe pulmonary vascular disease, worse RV function, and mortality independent of other sex hormones in men and women with PAH. DHEA has direct effects on nitric oxide (NO) and endothelin-1 (ET-1) synthesis and signaling, direct antihypertrophic effects on cardiomyocytes, and mitigates oxidative stress. Effects of Dehydroepiandrosterone in Pulmonary Hypertension (EDIPHY) is an on-going randomized double-blind placebo-controlled crossover trial of DHEA in men ( n = 13) and pre- and post-menopausal women ( n = 13) with Group 1 PAH funded by the National Heart, Lung and Blood Institute. We will determine whether orally administered DHEA 50 mg daily for 18 weeks affects RV longitudinal strain measured by cardiac magnetic resonance imaging, markers of RV remodeling and oxidative stress, NO and ET-1 signaling, sex hormone levels, other PAH intermediate end points, side effects, and safety. The crossover design will elucidate sex-based phenotypes in PAH and whether active treatment with DHEA impacts NO and ET-1 biosynthesis. EDIPHY is the first clinical trial of an endogenous sex hormone in PAH. Herein we present the study’s rationale and experimental design.

Digital image forensics has become necessary as an emerging technology. Images can be adulterated effortlessly using image tools. The latest techniques are available to detect whether an image is adulterated by a particular operator. Most of the existing techniques are suitable for high resolution and manipulated images by a single operator. In a real scenario, multiple operators are applied to manipulate the image many times. In this paper, a robust moderate-sized convolutional neural network is proposed to identify manipulation operators and also the operator’s sequence for two operators in particular. The proposed bottleneck approach is used to make the network deeper and reduce the computational cost. Only one pooling layer, called a global averaging pooling layer, is utilized to retain the maximum flow of information and to avoid the overfitting issue between the layers. The proposed network is also robust against low resolution and JPEG compressed images. Even though the detection of the operator is challenging due to the limited availability of statistical information in low resolution and JPEG compressed images, the proposed model can also detect an operator with different parameters and compression quality factors that are not considered in training.

In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Furthermore, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users’ tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD-DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weightage to the recent ratings. This non-noisy weighted matrix is fed into the Deep Learning model, consisting of an input layer, a Multi-Layer Perceptron, and an output layer to generate predicted ratings. The model’s performance is tested on three benchmark datasets, and experimental results confirm that TD-DNN outperforms other existing approaches.

Hiccups are manifestation of uremic syndrome. Baclofen had been used in uremic hiccups, but its usage has been described in literature with development of neurotoxicity in patients with renal failure. The case series reports three patients of Chronic Kidney Disease (CKD) that were on haemodialysis. All of them had intractable hiccups, for which Baclofen was administered. They developed encephalopathy within 24-48 hours of ingestion of Baclofen. The drug was discontinued and the patients improved within 36-48 hours. They were finally discharged with no residual neurological deficit.

Polo-like kinase 1 (PLK1) is an essential cell cycle mitotic kinase component that plays an important role in cell cycle progression and has been reported to be involved in various cancers, including neuroblastoma (NB). PLK1 also regulates G2/M transition, chromosomal segregation, spindle assembly maturation, and mitotic exit. NB is an early embryonic-stage heterogeneous solid tumor and accounts for 15% of all pediatric cancer-related deaths. Therefore, we aimed to develop a targeting strategy for PLK1 by repurposing HMN-214 in NB. HMN-214 is a prodrug of HMN-176 and is known to selectively interfere with PLK1 function. In the present study, we performed the transcriptomic analysis of a large cohort of primary NB patient samples and revealed that PLK1 expression is inversely correlated with the overall survival of NB patients. Additionally, we found that PLK1 strongly correlates with NB disease and stage progression. HMN-214 significantly inhibited NB proliferation and colony formation in both MYCN-amplified and -nonamplified cell lines in a dose-dependent manner. Furthermore, HMN-214 induces apoptosis and significantly obstructs the cell cycle at the G2/M phase in NB cells by inhibiting multiple cell-cycle-related genes, such as PLK1, WEE1, CDK1, CDK2, Cyclin B1, CHK1, and CHK2. HMN-214 significantly inhibits cell cycle regulator CDK1 and the phosphorylation and activation of PLK1 in NB. In the NB 3D spheroid tumor model, HMN-214 significantly and in a dose-dependent manner inhibits spheroid tumor mass and growth. Overall, our study highlights that targeting PLK1 using HMN-214 is a novel therapeutic approach for NB.

Introduction: Proximal tibia intra-articular fractures are commonly associated with ligamentous injuries and require early diagnosis and intervention. An array of fracture patterns can be encountered, and Schatzker's classification is a standard description to identify them. However, several patterns have been described that fall outside the ambits of this classification. We present a rare fracture pattern of partial posteromedial condyle coronal split associated with depression of the anterolateral plateau and mid substance tear of medial collateral ligament (MCL) and avulsions of anterior and posterior cruciate ligaments(ACL/PCL). Case: A 45 year old male presented with swelling and severe pain in his right knee after an accident. He was diagnosed with a posteromedial tibial condyle coronal split with two fragments, along with multi-ligament injuries. He was operated with cannulated screws and a rim plate for the larger fragment. The fractures united and patient was doing well in his daily activities at final follow up of 15 months. Conclusion: Proximal tibia fractures can present as an array of fracture patterns and require prompt diagnosis and surgical management. The one described in the present case is a rarest of rare pattern and rim plating could be a cheaper alternative to the anatomical plates. Keywords: Multi-ligamentous injury, Hoffa's, Proximal tibia, Posteromedial coronal split, Rim plating

Median filtering is being used extensively for image enhancement and anti-forensics. It is also being used to disguise the traces of image processing operations such as JPEG compression and image resampling when utilized in image de-noising and smoothing tool. In this paper, a robust image forensic technique namely HSB-SPAM is proposed to assist in median filtering detection. The proposed technique considers the higher significant bit-plane (HSB) of the image to highlight the statistical changes efficiently. Further, multiple difference arrays along with the first order pixel difference is used to separate the pixel difference, and Laplacian pixel difference is applied to extract a robust feature set. To compact the size of feature vectors, the operation of thresholding on the difference arrays is also utilized. As a result, the proposed detector is able to detect median, mean and Gaussian filtering operations with higher accuracy than the existing detectors. In the experimental results, the performance of the proposed detector is validated on the small size and post JPEG compressed images, where it is shown that the proposed method outperforms the state of art detectors in the most of the cases.

Novel coronavirus (COVID-19 or 2019-nCoV or SARS-CoV-2), which suddenly emerged in December 2019 is still haunting the entire human race and has affected not only the healthcare system but also the global socioeconomic balances. COVID-19 was quickly designated as a global pandemic by the World Health Organization as there have been about 98.0 million confirmed cases and about 2.0 million confirmed deaths, as of January 2021. Although, our understanding of COVID-19 has significantly increased since its outbreak, and multiple treatment approaches and pharmacological interventions have been tested or are currently under development to mitigate its risk-factors. Recently, some vaccine candidates showed around 95% clinical efficacy, and now receiving emergency use approvals in different countries. US FDA recently approved BNT162 and mRNA-1273 vaccines developed by Pfizer/BioNTech and Moderna Inc. for emergency use and vaccination in the USA. In this review, we present a succinct overview of the SARS-CoV-2 virus structure, molecular mechanisms of infection, COVID-19 epidemiology, diagnosis, and clinical manifestations. We also systematize different treatment strategies and clinical trials initiated after the pandemic outbreak, based on viral infection and replication mechanisms. Additionally, we reviewed the novel pharmacological intervention approaches and vaccine development strategies against COVID-19. We speculate that the current pandemic emergency will trigger detailed studies of coronaviruses, their mechanism of infection, development of systematic drug repurposing approaches, and novel drug discoveries for current and future pandemic outbreaks.

Despite largely unproven clinical effectiveness, incentive spirometry (IS) is widely used in an effort to reduce postoperative pulmonary complications. The objective of the study is to evaluate the financial impact of implementing IS. The amount of time nurses and RTs spend each day doing IS-related activities was assessed utilizing an online survey distributed to the relevant national nursing and respiratory therapists (RT) societies along with questionnaire that was prospectively collected every day for 4 weeks at a single 10-bed cardiothoracic surgery step-down unit. Cost of RT time to teach IS use to patients and cost of nurse time spent reeducating and reminding patients to use IS were used to calculate IS implementation cost estimates per patient. Per-patient cost of IS implementation ranged from $65.30 to $240.96 for a mean 9-day step-down stay. For the 566 patients who stayed in the 10-bed step-down in 2016, the total estimated cost of implementing IS ranged from $36 959.80 to $136 383.36. Using national survey workload data, per-patient cost of IS implementation costed $107.36 (95% confidence interval [CI], $97.88-$116.98) for a hospital stay of 4.5 days. For the 9.7 million inpatient surgeries performed annually in the United States, the total annual cost of implementing postoperative IS is estimated to be $1.04 billion (95% CI, $949.4 million-$1.13 billion). The cost of implementing IS is substantial. Further efficacy studies are necessary to determine whether the cost is justifiable.

Summary: Mutations in a number of chromatin modifiers are associated with human neurological disorders. KDM5C, a histone H3 lysine 4 di- and tri-methyl (H3K4me2/3)-specific demethylase, is frequently mutated in X-linked intellectual disability (XLID) patients. Here, we report that disruption of the mouse Kdm5c gene recapitulates adaptive and cognitive abnormalities observed in XLID, including impaired social behavior, memory deficits, and aggression. Kdm5c-knockout brains exhibit abnormal dendritic arborization, spine anomalies, and altered transcriptomes. In neurons, Kdm5c is recruited to promoters that harbor CpG islands decorated with high levels of H3K4me3, where it fine-tunes H3K4me3 levels. Kdm5c predominantly represses these genes, which include members of key pathways that regulate the development and function of neuronal circuitries. In summary, our mouse behavioral data strongly suggest that KDM5C mutations are causal to XLID. Furthermore, our findings suggest that loss of KDM5C function may impact gene expression in multiple regulatory pathways relevant to the clinical phenotypes. : In this study, Iwase et al. characterize Kdm5c-knockout mice to model an important class of intellectual disability. Kdm5c-knockout mice show limited learning, heightened aggression, and dendritic spine defects. Kdm5c is a histone demethylase, and the authors identify altered transcriptional profiles in Kdm5c-knockout brains and investigate the molecular changes in neurons.

Methotrexate is being used for many years in the treatment of chronic medical disorders e.g. rheumatoid arthritis since 1951. It has been associated with various systemic toxicities and complications including bone marrow suppression and lymphomas. The development of leukaemia in a patient of chronic rheumatoid arthritis is either related with the primary disease or due to the drugs which are used in the treatment like cyclophosphamide. In our present case, a 70-year-old female who was a known case of Rheumatoid Arthritis (RA) and was on methotrexate once a week orally for the past 20 years presented with complaints of loss of appetite, loss of weight and anaemia since 2 months. After thorough examination and investigation, she was diagnosed with acute myeloid leukaemia (AML-M4) with bilateral chest consolidation.

Piebaldism is a rare autosomal dominant disorder of melanocyte development characterized by a congenital white forelock and multiple symmetrical stable hypopigmented or depigmented macules. We report a family with piebaldism affecting three successive generations and also review the literature.

Liposomal chemotherapeutics are exemplified by DOXIL® are commonly used in adult cancers. While these agents exhibit improved safety profile compared to their free drug counterparts, their treatment response rates have been ~ 20%, often attributed to the heterogeneous intratumoral uptake and distribution of liposomal nanoparticles. Non-invasive and quantitative monitoring of the uptake and distribution of liposomal nanoparticles in solid tumors could allow for patient stratification and personalized cancer nanomedicine. In this study, the variability of liposomal nanoparticle intratumoral distribution and uptake in orthotopic models of pediatric neuroblastoma was investigated using a liposomal nanoprobe visualized by high-resolution computed tomography (CT). Two human neuroblastoma cell lines (NGP: a MYCN-amplified line, and SH-SY5Y a MYCN non-amplified line) were implanted in the renal capsule of nude mice to establish the model. Intratumoral nanoparticle uptake was measured at tumor ages 1, 2, 3 and 4 weeks post implantation. The locations of uptake within the tumor were mapped in the 3-dimensional reconstructed images. Total uptake was measured by integration of the x-ray absorption signal over the intratumoral uptake locations. Both tumor models showed significant variation in nanoparticle uptake as the tumors aged. Observation of the uptake patterns suggested that the nanoparticle uptake was dominated by vascular leak at the surface/periphery of the tumor, and localized, heterogeneous vascular leak in the interior of the tumor. Slow growing SH-SY5Y tumors demonstrated uptake that correlated directly with the tumor volume. Faster growing NGP tumor uptake did not correlate with any tumor geometric parameters, including tumor volume, tumor surface area, and R30 and R50, measures of uptake localized to the interior of the tumor. However, uptake for both SH-SY5Y and NGP tumors correlated almost perfectly with the leak volume, as measured by CT. These results suggest that the uptake of nanoparticles is heterogeneous and not governed by tumor geometry. An imaging nanoprobe remains the best measure of nanoparticle uptake in these tumor models.

Hypohidrotic ectodermal dysplasia (HED) is a rare genetic disorder characterized by the faulty development of the ectodermal structure, resulting in most notably anhydrosis/hypohydrosis, hypotrichosis and hypodontia. The condition is usually an X-linked recessive disorder affecting predominantly males. We are here reporting a classical case of hypohidrotic ectodermal dysplasia with a review of the literature.