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Ghrelin exerts key effects on islet hormone secretion to regulate blood glucose levels. Here, we sought to determine whether ghrelin's effects on islets extend to the alteration of islet size and ß cell mass. We demonstrate that reducing ghrelin -- by ghrelin gene knockout (GKO), conditional ghrelin cell ablation, or high-fat diet (HFD) feeding -- was associated with increased mean islet size (up to 62%), percentage of large islets (up to 854%), and ß cell cross-sectional area (up to 51%). In GKO mice, these effects were more apparent in 10-to 12-week-old mice than in 4-week-old mice. Higher ß cell numbers from decreased ß cell apoptosis drove the increase in ß cell cross-sectional area. Conditional ghrelin cell ablation in adult mice increased the ß cell number per islet by 40% within 4 weeks. A negative correlation between islet size and plasma ghrelin in HFD-fed plus chow-fed WT mice, together with even larger islet sizes in HFD-fed GKO mice than in HFD-fed WT mice, suggests that reduced ghrelin was not solely responsible for diet-induced obesity-associated islet enlargement. Single-cell transcriptomics revealed changes in gene expression in several GKO islet cell types, including upregulation of Manf, Dnajc3, and Gnas expression in ß cells, which supports decreased ß cell apoptosis and/or increased ß cell proliferation. These effects of ghrelin reduction on islet morphology might prove useful when designing new therapies for diabetes. [ABSTRACT FROM AUTHOR]

Smart sensing devices enabled hydroponics, a concept of vertical farming that involves soilless technology that increases green area. Although the cultivation medium is water, hydroponic cultivation uses 13 ± 10 times less water and gives 10 ± 5 times better quality products compared with those obtained through the substrate cultivation medium. The use of smart sensing devices helps in continuous real-time monitoring of the nutrient requirements and the environmental conditions required by the crop selected for cultivation. This, in turn, helps in enhanced year-round agricultural production. In this study, lettuce, a leafy crop, is cultivated with the Nutrient Film Technique (NFT) setup of hydroponics, and the growth results are compared with cultivation in a substrate medium. The leaf growth was analyzed in terms of cultivation cycle, leaf length, leaf perimeter, and leaf count in both cultivation methods, where hydroponics outperformed substrate cultivation. The results of the 'AquaCrop simulator also showed similar results, not only qualitatively and quantitatively, but also in terms of sustainable growth and year-round production. The energy consumption of both the cultivation methods is compared, and it is found that hydroponics consumes 70 ± 11 times more energy compared to substrate cultivation. Finally, it is concluded that smart sensing devices form the backbone of precision agriculture, thereby multiplying crop yield by real-time monitoring of the agronomical variables. [ABSTRACT FROM AUTHOR]

Background:Body fluids including pleural, peritoneal, and pericardial fluids accumulated in pathologic conditions, including benign and nonneoplastic disorders, and benign and malignant neoplasms. The present study was clinico--cytopathologic study of pericardial effusion. Materials & Methods:37 pericardial effusion specimens of both genders was recorded. Parameters such as cytologic features and the cytologic diagnosis of PE was classified into five categories in accordance with ISRSFC. Results: Out of 37 specimens, males were 20 and females were 17. Diagnosis was negative for malignancy (NFM) in 30, atypia of undetermined significance (AUS) in 4, suspicious for malignancy (SFM) in 2 and positive for malignancy (MAL) in 1 case. The difference was significant (P< 0.05). Symptoms/signs were raised jugular venous pulse in 24, breathlessness in 12, tachycardia in 15, tachypnea in 23, fever in 8, hypotension in 4, cough in 10 and body swelling in 3. Etiology was unknown in 15, Tb pericarditis in 6, traumatic in 8, heart disease in 1, renal disease in 1, autoimmune in 3 and malignancy/Suspicion for malignancy in 3 cases. The difference was non-significant (P> 0.05). Conclusion: PF specimens are uncommon. Unknown etiology the most common cause to produce malignant PF in both males and females. [ABSTRACT FROM AUTHOR]

The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation. [ABSTRACT FROM AUTHOR]

Supply chain management is implementing the standards of Industry 4.0 in order to enhance the trust and safety level in the system with invention of the latest technologies. At the same time, the customers are now more focused towards the product quality. Supply chain management is the demand of the era. However, it is difficult to trace and maintain the track data throughout the process during the supply of a product. The traditional solutions were based on centralized third party solutions, where the data is inclined to various security attacks and high risk of tempering. In this paper, a solution for the management of supply of a product has been proposed which is based on IoT with an amalgamation of blockchain so as to increase the transparency of the flow during the supply of the product. The solution will help to enhance the trust as the data will be shared among all participating nodes of the network in blockchain. It also makes instant traceability of the product and this can be done by both the end of the chain. Moreover, this solution will help the consumer to check whether the product is from a reliable source or not. For the implementation and testing of smart contracts the authors have used Remix IDE, MetaMask and RinkeyBy. [ABSTRACT FROM AUTHOR]

A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy. [ABSTRACT FROM AUTHOR]

Since the emergence of the first case of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2), the viral genome has constantly undergone rapid mutations for better adaptation in the host system. These newer mutations have given rise to several lineages/ variants of the virus that have resulted in high transmission and virulence rates compared to the previously circulating variants. Owing to this, the overall caseload and related mortality have tremendously increased globally to > 233 million infections and > 4.7 million deaths as of Sept. 28th, 2021. SARS-CoV-2, Spike (S) protein binds to host cells by recognizing human angiotensin-converting enzyme 2 (hACE2) receptor. The viral S protein contains S1 and S2 domains that constitute the binding and fusion machinery, respectively. Structural analysis of viral S protein reveals that the virus undergoes conformational flexibility and dynamicity to interact with the hACE2 receptor. The SARS-CoV-2 variants and mutations might be associated with affecting the conformational plasticity of S protein, potentially linked to its altered affinity, infectivity, and immunogenicity. This review focuses on the current circulating variants of SARS-CoV-2 and the structure–function analysis of key S protein mutations linked with increased affinity, higher infectivity, enhanced transmission rates, and immune escape against this infection. [ABSTRACT FROM AUTHOR]

The increasing popularity and adoption of the Internet of Things (IoT) ecosystem in various domains has brought attention to the security breaches linked with this paradigm. As the number of IoT devices continues to grow, it is essential to ensure that they are secured to protect against potential threats and attacks. IoT network proliferation of interconnected devices has significantly raised security concerns making them attractive targets for cyber attackers seeking to gain unauthorized access to systems and cause disruptions. As IoT networks collect and transmit sensitive data using centralized architecture, ensuring security and integrity of these networks becomes paramount. Distributed Ledger Technology (DLT) has emerged as a promising solution for enhancing IoT security. Two prominent DLT platforms: Blockchain and Internet of Things Application (IOTA) technologies can provide a more secure and resilient foundation for IoT ecosystems, and also help to mitigate risks associated with central node vulnerabilities. DLT-based IoT systems can also enable the creation of decentralized marketplaces and autonomous agents that can operate without human intervention. The objective of this research is to offer a comprehensive as well as fundamental study of IoT ecosystems and its associated security risks. Moreover, this paper provides a holistic study of the DLT platform and bibliometric inspection using VoS viewer tool on generic DLT platform technologies i.e., Blockchain and IOTA for securing data in IoT ecosystem. By leveraging bibliometric insights resulting from both DLT technologies, this study identities the most promising areas for further investigation and contribute to advancing security in IoT ecosystems. This survey contributes to the ongoing discourse on IoT security by providing a thorough comprehensive comparison of DLT solutions i.e., Blockchain and IOTA technologies on various key metrics, revealing that IOTA technology is projected to offer significant improvements over blockchain in securing sustainable IoT ecosystems. [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) cancer is leading general tumour in the Gastrointestinal tract, which is fourth significant reason of tumour death in men and women. The common cure for GI cancer is radiation treatment, which contains directing a high-energy X-ray beam onto the tumor while avoiding healthy organs. To provide high dosages of X-rays, a system needs for accurately segmenting the GI tract organs. The study presents a UMobileNetV2 model for semantic segmentation of small and large intestine and stomach in MRI images of the GI tract. The model uses MobileNetV2 as an encoder in the contraction path and UNet layers as a decoder in the expansion path. The UW-Madison database, which contains MRI scans from 85 patients and 38,496 images, is used for evaluation. This automated technology has the capability to enhance the pace of cancer therapy by aiding the radio oncologist in the process of segmenting the organs of the GI tract. The UMobileNetV2 model is compared to three transfer learning models: Xception, ResNet 101, and NASNet mobile, which are used as encoders in UNet architecture. The model is analyzed using three distinct optimizers, i.e., Adam, RMS, and SGD. The UMobileNetV2 model with the combination of Adam optimizer outperforms all other transfer learning models. It obtains a dice coefficient of 0.8984, an IoU of 0.8697, and a validation loss of 0.1310, proving its ability to reliably segment the stomach and intestines in MRI images of gastrointestinal cancer patients. [ABSTRACT FROM AUTHOR]

Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey. [ABSTRACT FROM AUTHOR]

Brain tumor diagnosis at an early stage can improve the chances of successful treatment and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures, such as magnetic resonance imaging (MRI), can be used to diagnose brain tumors. Deep learning, a type of artificial intelligence, can analyze MRI images in a matter of seconds, reducing the time it takes for diagnosis and potentially improving patient outcomes. Furthermore, an ensemble model can help increase the accuracy of classification by combining the strengths of multiple models and compensating for their individual weaknesses. Therefore, in this research, a weighted average ensemble deep learning model is proposed for the classification of brain tumors. For the weighted ensemble classification model, three different feature spaces are taken from the transfer learning VGG19 model, Convolution Neural Network (CNN) model without augmentation, and CNN model with augmentation. These three feature spaces are ensembled with the best combination of weights, i.e., weight1, weight2, and weight3 by using grid search. The dataset used for simulation is taken from The Cancer Genome Atlas (TCGA), having a lower-grade glioma collection with 3929 MRI images of 110 patients. The ensemble model helps reduce overfitting by combining multiple models that have learned different aspects of the data. The proposed ensemble model outperforms the three individual models for detecting brain tumors in terms of accuracy, precision, and F1-score. Therefore, the proposed model can act as a second opinion tool for radiologists to diagnose the tumor from MRI images of the brain. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID- 19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread over the world. Although there are minimal microbiological and antibiotic data on COVID-19, bacterial co-infections have been related to poor outcomes in respiratory viral infections. Adequate antibiotic use in conformity with antibiotic stewardship (ABS) recommendations is necessary during the pandemic. Material and procedure: We conducted a retrospective single-center cohort analysis of 140 adult hospitalised patients (ages 17-99) with confirmed COVID-19 who were admitted between February 16, 2021, and April 22, 2021, and who were discharged on May 6, 2021. From 140 COVID-19 participants, the following clinical data was gathered: Men made up 63.5 percent of the participants, with a median age of 63.5 years (range 17-99). Results: According to local ABS recommendations, the most commonly administered antibiotic regimen was ampicillin/sulbactam (41.5 percent) with a median length of 6 (range 1-13) days. Urine antigen testing for Legionella pneumophila and Streptococcus peumoniae was negative in all of the patients. In critically ill patients hospitalised to intensive care units (n = 50), co-infections with Enterobacterales (34.0%) and Aspergillus fumigatus (18.0%) were discovered. Blood cultures obtained at admission had a diagnostic yield of 4.2 percent. Conclusion: While bacterial and fungal co-infections are rare in COVID-19 patients, they are widespread in critically ill individuals. More investigation into the impact of antimicrobial therapy on therapeutic success in COVID-19 patients is essential to prevent antibiotic abuse.COVID-19 management might be improved with the aid of ABS standards. It's also necessary to look at the microbiological patterns of infectious consequences in COVID-19 individuals who are severely unwell. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brain's ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Naïve base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective. [ABSTRACT FROM AUTHOR]

When it comes to software development, precise planning, proper documentation and proper process control, some errors are inevitable in the software environment. These software flaws can lead to quality deterioration, which can be the main reason behind system failure. As the whole world especially developing countries is dependent upon software systems, it is very important to focus on its reliability aspect. Nowadays sophisticated systems require concerted efforts for managing and reducing the shortcomings in software engineering. But, these efforts require more cost, more money and more time. Software error prediction is the most helpful step in the testing stage of the software development life cycle. It identifies components or parts of the code where an error may occur and requires broad testing, so the test resources can be efficiently used. Software error assessment reduces efforts of testing the software by helping the software testers locate the actual problem and classify different classes of errors in the system. Error estimators are majorly used in various organizations to evaluate the software to save time, improve the quality of software and testing and optimize resources to meet timelines. Machine learning provides support in fault projection by collecting the training data from various edge devices and thus helps in escalating the reliability of the software available on Kaggle. The multilayer perceptron shows better results in precision, recall, F1 score and accuracy as compared to decision tree and Gaussian Naive Bayes as it achieves an accuracy of 96.8%. [ABSTRACT FROM AUTHOR]

Nowadays, one of the most popular applications is cloud computing for storing data and information through World Wide Web. Since cloud computing has become available, users are rapidly increasing. Cloud computing enables users to obtain a better and more effective application at a lower cost in a more satisfactory way. Health services data must therefore be kept as safe and secure as possible because the release of this data could have serious consequences for patients. A framework for security and privacy must be employed to store and manage extremely sensitive data. Patients' confidential health records have been encrypted and saved in the cloud using cypher text so far. To ensure privacy and security in a cloud computing environment is a big issue. The medical system has been designed as a standard, access of records, and effective use by medical practitioners as required. In this paper, we propose a novel algorithm along with implementation details as an effective and secure E-health cloud model using identity-based cryptography. The comparison of the proposed and existing techniques has been carried out in terms of time taken for encryption and decryption, energy, and power. Decryption time has been decreased up to 50% with the proposed method of cryptography. As it will take less time for decryption, less power is consumed for doing the cryptography operations. [ABSTRACT FROM AUTHOR]

Cloud computing provides unprecedented advantages of using computing resources with very less efforts and cost. The energy utilization in cloud data centers has forced the cloud service providers to raise the expense of using its services and has increased the carbon footprints in the environment. Many static bin-packing algorithms exist which can reduce energy by some percentage, but with new era of digitization, advanced and dynamic techniques are required which can serve heterogeneous users and random users' requests. Thus, in this paper, two new dynamic best-fit decreasing-based bin-packing algorithms are proposed wherein the first technique is for service providers and focuses on increasing server utilization and the second approach acts as a switcher to harness best results among all algorithms. Both techniques deliberately achieve high performance in terms of total energy consumption, resource utilization, and makespan along with serving continuous and varying requests from customers. The simulations are performed using Java. The results exhibited that DEE-BFD can escalate resource utilization by 96% and EM switcher can reduce total energy consumption by 49% and reduce makespan by 56%. [ABSTRACT FROM AUTHOR]

Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is utilized. These blood cell images are subjected to various data analyzing techniques that count and classify the different types of blood cells. Nowadays, deep learning-based methods are in practice to analyze the data. These methods are less time-consuming and require less sophisticated equipment. This paper implements a deep learning (D.L) model that uses the DenseNet121 model to classify the different types of white blood cells (WBC). The DenseNet121 model is optimized with the preprocessing techniques of normalization and data augmentation. This model yielded an accuracy of 98.84%, a precision of 99.33%, a sensitivity of 98.85%, and a specificity of 99.61%. The proposed model is simulated with four batch sizes (BS) along with the Adam optimizer and 10 epochs. It is concluded from the results that the DenseNet121 model has outperformed with batch size 8 as compared to other batch sizes. The dataset has been taken from the Kaggle having 12,444 images with the images of 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. With such results, these models could be utilized for developing clinically useful solutions that are able to detect WBC in blood cell images. [ABSTRACT FROM AUTHOR]

Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model's capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model's superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches. [ABSTRACT FROM AUTHOR]

Background The prevention of coronavirus disease 2019 (COVID-19) in vulnerable populations is a global health priority. EVADE was a phase 2/3 multicenter, double-blind, randomized, placebo-controlled trial of adintrevimab, an extended–half-life monoclonal antibody, for postexposure (PEP) and pre-exposure prophylaxis (PrEP) of symptomatic COVID-19. Methods Eligible participants (vaccine-naive, aged ≥12 years) were randomized 1:1 to receive a single 300-mg intramuscular injection of adintrevimab or placebo. Primary efficacy end points were reverse transcription polymerase chain reaction (RT-PCR)–confirmed symptomatic COVID-19 through day 28 in the PEP cohort (RT-PCR-negative at baseline) and through month 3 in the PrEP cohort (RT-PCR-negative and seronegative at baseline) among participants randomized before emergence of the severe acute respiratory syndrome coronavirus 2 Omicron variant (November 30, 2021). Safety was assessed through 6 months. Results Between April 27, 2021, and January 11, 2022, 2582 participants were randomized. In the primary efficacy analysis, RT-PCR-confirmed symptomatic COVID-19 occurred in 3/175 (1.7%) vs 12/176 (6.8%) adintrevimab- and placebo-treated PEP participants, respectively (74.9% relative risk reduction [RRR]; standardized risk difference, −5.0%; 95% CI, −8.87% to −1.08%; P =.0123) and in 12/752 (1.6%) vs 40/728 (5.5%) adintrevimab- and placebo-treated PrEP participants, respectively (71.0% RRR; standardized risk difference, −3.9%; 95% CI, −5.75% to −2.01%; P <.0001). In a prespecified exploratory analysis of 428 PrEP participants randomized after the emergence of Omicron, adintrevimab reduced RT-PCR-confirmed symptomatic COVID-19 by 40.6% (standardized risk difference −8.4%; 95% CI, −15.35% to −1.46%; nominal P =.0177) vs placebo. Adintrevimab was well tolerated, with no serious drug-related adverse events reported. Conclusions A single intramuscular injection of adintrevimab provided prophylactic efficacy against COVID-19 due to susceptible variants without safety concerns. Clinical trial registration. NCT04859517. [ABSTRACT FROM AUTHOR]

Background Safe and effective treatments are needed to prevent severe outcomes in individuals with coronavirus disease 2019 (COVID-19). We report results from STAMP, a phase 2/3, multicenter, double-blind, randomized, placebo-controlled trial of adintrevimab, an extended half-life monoclonal antibody, for treatment of high-risk ambulatory patients with mild to moderate COVID-19. Methods Nonhospitalized, unvaccinated participants aged ≥12 years with mild to moderate COVID-19 and ≥1 risk factor for disease progression were randomized to receive a single intramuscular injection of 300 mg adintrevimab or placebo. Enrollment was paused due to the global emergence of the Omicron BA.1/BA1.1 variants, against which adintrevimab showed reduced activity in vitro. The primary efficacy endpoint was COVID-19–related hospitalization or all-cause death through day 29 in participants with COVID-19 due to laboratory-confirmed or suspected non-Omicron severe acute respiratory syndrome coronavirus 2 variants. Results Between 8 August 2021 and 11 January 2022, 399 participants were randomized to receive adintrevimab (n = 198) or placebo (n = 201), including 336 with COVID-19 due to non-Omicron variants. COVID-19–related hospitalization or all-cause death through day 29 occurred in 8 of 169 (4.7%) participants in the adintrevimab group and 23 of 167 (13.8%) participants in the placebo group, a 66% relative risk reduction in favor of adintrevimab (standardized risk difference, −8.7% [95% confidence interval, −14.71% to −2.67%]; P =.0047). Incidence of treatment-emergent adverse events (TEAEs) was similar between treatment groups (33.9% for adintrevimab and 39.5% for placebo). No adintrevimab-related serious TEAEs were reported. Conclusions Treatment with a single intramuscular injection of adintrevimab provided protection against severe outcomes in high-risk ambulatory participants with COVID-19 due to susceptible variants, without safety concerns. Clinical Trial Registration. NCT04805671. [ABSTRACT FROM AUTHOR]

Introduction: Adintrevimab is a fully human immunoglobulin G1 extended half-life monoclonal antibody that was developed to have broad neutralization against SARS-CoV, SARS-CoV-2, and other SARS-like CoVs with pandemic potential. Here we report the safety, pharmacokinetics (PK), serum viral neutralizing antibody (sVNA) titers, and immunogenicity results of the first three cohorts evaluated in the first-in-human study of adintrevimab in healthy adults. Methods: This is a phase 1, randomized, placebo-controlled, single ascending-dose study of adintrevimab administered intramuscularly (IM) or intravenously (IV) to healthy adults aged ≥ 18–55 years with no current or prior SARS-CoV-2 infection. Participants were randomized 8:2 to adintrevimab or placebo in each of three dose cohorts: adintrevimab 300 mg IM (cohort 1), 500 mg IV (cohort 2), and 600 mg IM (cohort 3). Follow-up was 12 months. Blood samples were taken predose and at multiple time points postdose up to month 12 to assess sVNA, PK, and antidrug antibodies (ADAs). Results: Thirty participants received a single dose of adintrevimab (n = 24; 8 per cohort) or placebo (n = 6). All except one adintrevimab participant in cohort 1 completed the study. No participants in any treatment arm experienced a study drug-related adverse event. Across adintrevimab-treated participants, 11 (45.8%) experienced at least one TEAE. All but one TEAE were mild in severity, and all were either viral infection or respiratory symptoms. There were no serious adverse events, discontinuations due to adverse events, or deaths. Adintrevimab exhibited a linear and dose-proportional PK profile and extended serum half-life (mean 96, 89, and 100 days in cohorts 1, 2, and 3, respectively). Participants receiving adintrevimab demonstrated dose-dependent increased sVNA titers and breadth across multiple variants. Conclusion: Adintrevimab at doses of 300 mg IM, 500 mg IV, and 600 mg IM was well tolerated in healthy adults. Adintrevimab demonstrated dose-proportional exposure, rapid development of neutralizing antibody titers, and an extended half-life. [ABSTRACT FROM AUTHOR]

For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset. [ABSTRACT FROM AUTHOR]