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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]

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]

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]

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]

Insulin-induced hypoglycemia leads to far-ranging negative consequences in patients with diabetes. Components of the counterregulatory response (CRR) system that help minimize and reverse hypoglycemia and coordination between those components are well studied but not yet fully characterized. Here, we tested the hypothesis that acyl-ghrelin, a hormone that defends against hypoglycemia in a preclinical starvation model, is permissive for the normal CRR to insulin-induced hypoglycemia. Ghrelin knockout (KO) mice and wild-type (WT) littermates underwent an insulin bolus-induced hypoglycemia test and a low-dose hyperinsulinemic-hypoglycemic clamp procedure. Clamps also were performed in ghrelin-KO mice and C57BL/6N mice administered the growth hormone secretagogue receptor agonist HM01 or vehicle. Results show that hypoglycemia, as induced by an insulin bolus, was more pronounced and prolonged in ghrelin-KO mice, supporting previous studies suggesting increased insulin sensitivity upon ghrelin deletion. Furthermore, during hyperinsulinemic-hypoglycemic clamps, ghrelin-KO mice required a 10-fold higher glucose infusion rate (GIR) and exhibited less robust corticosterone and growth hormone responses. Conversely, HM01 administration, which reduced the GIR required by ghrelin-KO mice during the clamps, increased plasma corticosterone and growth hormone. Thus, our data suggest that endogenously produced acyl-ghrelin not only influences insulin sensitivity but also is permissive for the normal CRR to insulin-induced hypoglycemia. [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]

Background: The monkey malaria parasite Plasmodium knowlesi also infect humans. There is a lack of information on the molecular mechanisms that take place between this simian parasite and its heterologous human host erythrocytes leading to this zoonotic disease. Therefore, we investigated here the binding ability of P. knowlesi tryptophan-rich antigens (PkTRAgs) to the human erythrocytes and sharing of the erythrocyte receptors between them as well as with other commonly occurring human malaria parasites. Methods: Six PkTRAgs were cloned and expressed in E.coli as well as in mammalian CHO-K1 cell to determine their human erythrocyte binding activity by cell-ELISA, and in-vitro rosetting assay, respectively. Results: Three of six PkTRAgs (PkTRAg38.3, PkTRAg40.1, and PkTRAg67.1) showed binding to human erythrocytes. Two of them (PkTRAg40.1 and PkTRAg38.3) showed cross-competition with each other as well as with the previously described P.vivax tryptophan-rich antigens (PvTRAgs) for human erythrocyte receptors. However, the third protein (PkTRAg67.1) utilized the additional but different human erythrocyte receptor(s) as it did not cross-compete for erythrocyte binding with either of these two PkTRAgs as well as with any of the PvTRAgs. These three PkTRAgs also inhibited the P.falciparum parasite growth in in-vitro culture, further indicating the sharing of human erythrocyte receptors by these parasite species and the biological significance of this receptor-ligand interaction between heterologous host and simian parasite. Conclusions: Recognition and sharing of human erythrocyte receptor(s) by PkTRAgs with human parasite ligands could be part of the strategy adopted by the monkey malaria parasite to establish inside the heterologous human host. [ABSTRACT FROM AUTHOR]

Objective: In the last few decades, serotonin type-3 (5-HT3) receptor antagonists have been identified as potential targets for anxiety disorders. In preclinical studies, 5-HT3 antagonists have shown promising antianxiety effects. In this study, a novel 5-HT3 receptor antagonist, QCM-13(N-cyclohexyl-3-methoxyquinoxalin-2-carboxamide) was evaluated for anxiolytic-like activity in rodent behavioral test battery. Materials and Methods: Mice were given QCM-13 (2 and 4 mg/kg, intraperitoneally [i.p.]) or diazepam (2 mg/kg, i.p.) or vehicle and after 30 min, mice were subjected to four validated behavioral test batteries viz. elevated plus maze, hole board, light-dark and open field tests. Interaction study of QCM-13 with m-chlorophenyl piperazine (mCPP) (mCPP, a 5-HT2A/2C receptor agonist, 1 mg/kg, i.p.) and buspirone (BUS, a partial 5-HT1A agonist, 10 mg/kg, i.p.) were performed to assess the pharmacological mechanism of the drug. Results: QCM-13 expressed potential anxiolytic effect with significant (P < 0.05) increase in behavioral parameters measured in aforementioned preliminary models. Besides, QCM-13 was unable to reverse the anxiogenic effect of mCPP, but potentiated anxiolytic affect of BUS. Conclusion: The results suggest that QCM-13 can be a potential therapeutic candidate for the management of anxiety-like disorders and combination doses of novel 5-HT3 receptor antagonist with standard anxiolytics may improve therapeutic efficacy. [ABSTRACT FROM AUTHOR]

Diabetes associated depression is a largely understudied fi eld which nonetheless carries a signifi cant disease burden. The very low therapeutic effi cacy of the existing conventional drugs with poor outcome may be, in part, due to uncertainty of the mechanism involved that clearly explains the existing comorbidity. The main purpose of this review was to address the sophisticated mechanisms of this comorbidity with a view of developing potential novel targets with higher effi cacy and specifi city. Data were collected from database searches including PubMed, references from relevant English language research/review articles and other offi cial publications. Articles from 1990 to 2013 were included, and a broad search term criteria were followed for data mining so that relevant information was not missed out. Some of the search terms used included; diabetes-induced depression, diabetes and serotonin, hypothalamic-pituitary-adrenal (HPA) axis and diabetes and glucocorticoids in diabetes. Neuropathologically, depletion of brain monoaminergic activity specifi cally the serotonin (5-hydroxytryptamine [5-HT]) system, due to chronically persisting diabetic state may lead to the mood and behavioral complications that further add on worsening the quality life years. The 5-HT system through multifunctional tasks regulates neurogenesis and plasticity and by complex receptor mechanism controls the emotional and behavioral activity. Persisting hyperglycemia leads to impaired neurogenesis, decreased synaptic plasticity, undesired neuro-anatomical alterations, neurochemical defi cits, and reduced neurotransmitter activity. The neurotrophic factors and secondary messenger functions affected at molecular and genetic levels indicate the impact of diabetes-mediated dysregulation on neuronal circuits. HPA activity, glycogen synthase kinase 3, and insulin signaling controls were also found to be hampered, interlinked to 5-HT system following diabetic progression. [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]

Background: Depression is a neurological disorder characterized by sad mood, loss of pleasure, agitation and retardation. Though most relevant neuronal pathophysiology is characterized by decrease in monoamine namely; serotonin (5-HT), dopamine, noradrenaline level in central areas regulating mood and behavior, it inadequately explains the exact mechanism involved. Buspirone (BUS), a partial 5-HT1A receptor agonist has shown promising anti-depressant and anxiolytic properties in various pre-clinical and clinical studies, but the molecular and cellular mechanisms are still unclear. Objective: The aim of this study was to investigate, in vivo, the role of hypothalamic-pituitary-adrenal (HPA) axis dysregulation in pathophysiology of depression-related disorders and the anti-depressant like activity of BUS. To simulate HPA axis dysregulation, rats were subjected to bilateral adrenalectomy (ADX). Materials and Methods: We have analyzed effect of BUS (10 mg/kg, i.p.) in ADX and sham rats using open field, sucrose consumption, elevated plus maze and hyper-emotionality tests. Results: In all animal models tested, ADX rats exhibited significant depressive and anxiogenic states while BUS was effective in reversing the psychological diseased condition developed. Conclusion: Taken together, these data showed a prominent role of HPA axis in depression and neuronal mechanism of BUS as anti-depressant and anxiolytic agent. Moreover, our findings suggest that BUS can be a better candidate for stress related depression and anxiety. [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]

Objective: While expression of the ghrelin receptor, GHSR (growth hormone secretagogue receptor), by pancreatic islets has been investigated, data regarding the specific islet cell types that express GHSR are inconsistent and incomplete. For instance, transcriptomic studies indicate marked GHSR expression by somatostatin (SST)-secreting delta cells vs. little to no GHSR expression by alpha cells and beta cells, which contrasts studies suggesting direct actions of ghrelin on GHSRs expressed by alpha cells and beta cells. Also, to our knowledge, GHSR expression by pancreatic polypeptide (PP) cells has not previously been directly investigated. The aims of this study were to determine GHSR expression within the four principal islet endocrine cell types and the effects of ghrelin on circulating PP. Methods: Ghsr-IRES-Cre mice, which express Cre recombinase directed by the GHSR promoter, crossed to Cre-dependent ROSA26-YFP reporter mice were used to histologically identify GHSR expression in islets. Double and triple labeling for YFP-immunoreactivity (IR) with insulin-IR, glucagon-IR, SST-IR, and/or PP-IR was performed to determine GHSR co-expression with islet hormones. Pharmacological manipulations were used to examine in vivo effects of acyl-ghrelin on plasma PP. Results: Eighty-five % of YFP-IR cells expressed PP-IR, 50% expressed SST-IR, and none expressed insulin-IR or glucagon-IR. GHSR antagonist raised plasma PP in overnight-fasted C57BL/6N mice. However, acyl-ghrelin administration had no effect on plasma PP in C57BL/6N mice. Conclusions: These results suggest that within mouse pancreatic islets, GHSR expression occurs most abundantly in PP cells and that pharmacologic blockade of ghrelin action raises plasma PP. Disclosure: D. Gupta: None. B.K. Mani: None. K. Shankar: None. S. Osborne-Lawrence: None. N. Metzger: None. J.M. Zigman: Stock/Shareholder; Self; Medtronic. Funding: Diana and Richard C. Strauss Professorship in Biomedical Research; Mr. and Mrs. Bruce G. Brookshire Professorship in Medicine; Kent and Jodi Foster Distinguished Chair in Endocrinology; University of Texas Southwestern Medical Center (to J.M.Z.) [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]

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]

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]

The challenges involved in the traditional cloud computing paradigms have prompted the development of architectures for the next generation cloud computing. The new cloud computing architectures can generate and handle huge amount of data, which was not possible to handle with the help of traditional architectures. Deep learning algorithms have the ability to process this huge amount of data and, thus, can now solve the problem of the next generation computing algorithms. Therefore, these days, deep learning has become the state-of-the-art approach for solving various tasks and most importantly in the field of recognition. In this work, recognition of city names is proposed. Recognition of handwritten city names is one of the potential research application areas in the field of postal automation For recognition using a segmentation-free approach (Holistic approach). This proposed work demystifies the role of convolutional neural network (CNN), which is one of the methods of deep learning technique. Proposed CNN model is trained, validated, and analyzed using Adam and stochastic gradient descent (SGD) optimizer with a batch size of 2, 4, and 8 and learning rate (LR) of 0.001, 0.01, and 0.1. The model is trained and validated on 10 different classes of the handwritten city names written in Gurmukhi script, where each class has 400 samples. Our analysis shows that the CNN model, using an Adam optimizer, batch size of 4, and a LR of 0.001, has achieved the best average validation accuracy of 99.13. [ABSTRACT FROM AUTHOR]

One of the most economical and precious spices ever known is saffron. It has innumerable pharmaceutical applications, and its cultivation is limited to certain topographic regions throughout the world. Although efforts are continuously being made to increase productivity and increase the area under cultivation, there are multiple factors leading to its low production. Corm size is one of the major factors affecting the quality and production of saffron. The more the corm size for a plantation, the better the yield of the crop. This can pose a serious threat to saffron cultivators who are not able to get good quality corms and sort quality corms from infected ones. Additional factors affecting saffron cultivation include attack by fungus leading to corm rots and sclerotineal disease. In this paper, IoT (Internet of Things) has been used to provide an energy-efficient, readyto-use, reliable, and user-friendly solution for corm sorting and treatment before sowing to promote quality, better yield and minimized wastage due to corm rots. After sorting the corms, they are treated with 0.2% of tiabendazole fungicide solution and dried, before sowing and for long-term storage. Firstly, sorting and treatment system for the saffron corms processing as per weight is proposed. First two-layer cleaning mechanism to remove foreign substances, then three-step treatment has been applied to remove microorganisms. An automated conveyor system equipped with IoT devices was used for sorting and treating saffron corms. Different energy efficient LPWA (Low Power Wide Area) and LTE (Long Term Evolution) technologies used in the framework, for communication are NB-IoT (Narrow Band IoT) and LoRa (Long Range Radio). [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]

A good contrast is significant for analysis of medical images, and if the images have poor contrast, then some methods of contrast enhancement can be of much benefit. In this paper, a convolution neural network-based transfer learning approach is utilized for contrast enhancement of mammographic images. The experiments are conducted on ISP and MIAS datasets, where ISP dataset is used for training and MIAS dataset is used for testing (contrast enhancement). Experimental comparison of the proposed technique is done with the most popular direct and indirect contrast enhancement techniques such as CLAHE, BBHE, RMSHE, and contrast stretching. A qualitative comparison is done using mean square error (MSE), signal to noise ratio (SNR), and peak signal to noise ratio (PSNR). It is observed that the proposed technique outperforms the other techniques HE, RMSHE, CLAHE, BBHE, and contrast stretching. [ABSTRACT FROM AUTHOR]

Context: Anaesthesia during cleft lip and palate surgery carries a high risk and difficult airway management in children. Aim: to study the perioperative anesthetic complications in poor children with cleft abnormalities. Settings and Design: Retrospective analysis. Materials and Methods: This retrospective audit was conducted on 2917 patients of smile train project under going general anesthesia for cleft lip and palate from January 2007 to December 2010. Demographic, pre-anesthetic status, anesthetic management and anesthesia complications were recorded. Chi-square test was used to assess the relation between patient factors and occurrence of complications. Results: Of the 3044, we were able to procure complete data of 2917 patients. Most of children presented with anemia 251 (35%), 202 (29%) had eosinophilia while 184 (26%) had upper respiratory tract infection. The incidence of perioperative complications was 8.19% of which 33.7% critical incidents occurred during the induction time. The most common complication was laryngospasm 77 (40.9%) followed by difficult intubation 64 (30.9%). There was no mortality. Conclusion: Since these procedures do not characterize an emergency, most of the perioperative complications can be prevented by following the routine installed by the institute and smile train protocols. [ABSTRACT FROM AUTHOR]

Objective: The hormone ghrelin both regulates and is regulated by food intake and blood glucose. Yet, the mechanisms restricting ghrelin release after meals or upon glucose delivery have remained elusive. Here, we tested whether meal and glucose-associated rises in plasma insulin act via ghrelin cell-expressed insulin receptors to suppress ghrelin release. Methods: We generated ghrelin cell-selective insulin receptor-knock out (GIRKO) mice by crossing ghrelin-Cre mice with floxed-insulin receptor mice. We examined ghrelin release using hyperinsulinemic-hypoglycemic clamps, glucose tolerance tests (GTTs), a fast-refeed protocol, and gastric mucosal cell primary cultures. Results: During clamps, plasma ghrelin fell in WT control groups (by 85%) but not in GIRKO mice, leading to a 29% lower glucose infusion rate in GIRKO mice. During GTTs, plasma ghrelin was suppressed in controls (by 42%) but not in GIRKO mice. During the refeed phase of a fast-refeed protocol, plasma ghrelin fell in controls (by 49%) but not in GIRKO mice. Also, insulin suppressed ghrelin release in isolated ghrelin cells of controls (by 19%) but not of GIRKO mice. Conclusions: These data suggest that insulin, acting via ghrelin cell-expressed insulin receptors, is a key mediator of ghrelin release in response to meals, glucose administration, and insulin infusion. Further, suppressed ghrelin release resulting from direct insulin action on ghrelin cells restricts ghrelin's capacity to protect against hypoglycemia during over-insulinization. Disclosure: K. Shankar: None. S. Takemi: None. D. Gupta: None. B.K. Mani: None. S. Osborne-Lawrence: None. N. Metzger: None. E. Berglund: None. J.M. Zigman: Stock/Shareholder; Self; Medtronic. Funding: National Institutes of Health (R01DK119341-01A1, R01DK103884 to J.M.Z.), (R01DK109408 to E.B.), (R01DK119341 to J.M.Z., E.B.); David and Teresa Disiere Foundation (to J.M.Z.); Diana and Richard C. Strauss Professorship in Biomedical Research; Mr. and Mrs. Bruce G. Brookshire Professorship in Medicine; Kent and Jodi Foster Distinguished Chair in Endocrinology (to J.M.Z.); Uehara Memorial Foundation (to S.T.) [ABSTRACT FROM AUTHOR]

The essentiality of DNA Gyrase in basic cellular processes in bacterial pathogens makes it an ideal drug target. Though the Gyrase has a conserved mechanism of action, the complete DNA wrapping and binding process is still unknown. In this study, we have identified six arginine residues R556, R612, R667, R716, R766, and R817 in the DNA GyraseA – C-terminal domain from Salmonella enterica serovar Typhi (StGyrA-CTD) to be essential for DNA wrapping and sliding by a sequence and structure analysis. Through site-directed mutagenesis and EMSA studies, we observed that the substitution of R667 (blade 3) and R716 (blade 4) in StGyrA-CTD led to loss of DNA binding. Whereas, upon mutation of residue R612 (blade2), R766 (blade5) and R817 (blade6) along with supporting residue R712 (blade 4) a decrease in binding affinity was seen. Our results indicate that R667 and R716 act as a pivot point in DNA wrapping and sliding during gyrase catalytic activity. In this study, we propose that the DNA wrapping mechanism commences with DNA binding at blade3 and blade4 followed by other blades to facilitate the DNA sliding during supercoiling activity. This study provides a better understanding of the DNA binding and wrapping mechanism of GyrA-CTD in DNA Gyrase. [ABSTRACT FROM AUTHOR]

Objective: To investigate the antidepressant-like effect of N-(benzo[d] thiazol-2-yl)-3- ethoxyquinoxalin-2-carboxamide 6k, a 5-hydroxytryptamine type 3 (5-HT3) receptor antagonist using rodents behavioral battery tests. Materials and Methods: 6k screening was performed with behavioral assays for depression-like forced swim test (FST) at several single doses (0.25-4 mg/kg, intraperitoneal injection (i.p.)) to test the potency of 6k, in which 2 and 4 mg/kg doses were found to be most effective and hence, in further behavioral assays including mechanistic model like 5-hydroxytryptophan (5-HTP)-induced head twitches was performed in mice at acute doses of 6k (2 and 4 mg/kg, i.p.). Furthermore, olfactory bulbectomy (OBX), a surgical model-induced behavioral alterations was performed in rats, and the effect of 6k administered orally (2 and 4 mg/kg, p.o.) after subchronic treatment for 14 days starting from day 15 of postsurgery was examined by percent sucrose preference test and modified open field test (OFT). Results: 6k (1, 2, and 4 mg/kg, i.p.) reduced the immobility time and increased the swimming behavior in FST without affecting the baseline locomotor score showing antidepressant-like effect. 5-HTP-induced head twitch response was potentiated by 6k (2 and 4 mg/kg, i.p.), which indicated rise in the serotonergic neurotransmission in the brain. 6k (2 and 4 mg/kg, p.o.) showed anti-anhedonia effect by increasing the sucrose consumption and reversed the behavioral alterations when exposed to modified open field in OBX rats after subchronic treatment for 14 days, thus exhibiting antidepressant-like effect. Conclusion: 6k attenuated the behavioral derangement in rodents-based behavioral battery tests for depression, indicating antidepressant-like potential. [ABSTRACT FROM AUTHOR]

Objective: To investigate the anti-anxiety activity of "6k", a novel 5-hydroxytryptamine type 3 (5-HT3) receptor antagonist in in mice. Materials and Methods: Anti-anxiety activity of "6k" (1, 2, and 4 mg/kg, intraperitoneally (i.p.)) was evaluated in mice by behavioral tests such as elevated plus maze (EPM), open field test (OFT), light-dark box (L&D), and hole board test (HBT). Diazepam (2 mg/kg, i.p.) served as reference standard. Results: "6k" significantly (P < 0.05) increased the time and entries in open arm in EPM as compared to vehicle control group. Further, "6k" significantly (P < 0.05) increased the central and peripheral ambulation along with rearings and time in central area; whereas, reduced the fecal pellets in OFT as compared to vehicle control group. There was significant (P < 0.05) reduction in the latency to enter dark chamber; whereas, increased number of crossings and time in light chamber in L&D aversion test by treatment with "6k" as compared to vehicle control group. In HBT, "6k" significantly (P < 0.05) increased the number of head dipping and squares crossed; whereas, reduced the latency for first head dip and number of fecal pellets as compared to vehicle control group. Conclusion: A novel 5-HT3 receptor antagonist has anti-anxiety action. [ABSTRACT FROM AUTHOR]

To investigate the structure-function relationship of plant cyclic nucleotide-gated ion channels (CNGCs), we identified a total of 29 mutant alleles of the chimeric AtCNGC11/12 gene that induces multiple defense responses in the Arabidopsis (Arabidopsis thaliana) mutant, constitutive expresser of PR genes22 (cpr22). Based on computational modeling, two new alleles, $100 (AtCNGC11/12:G459R) and $137 (AtCNGC11/12:R381H), were identified as counterparts of human CNGA3 (a human CNGC) mutants. Both mutants lost all cpr22-mediated phenotypes. Transient expression in Nicotiana benthamiana as well as functional complementation in yeast (Saccharomyces cerevisiae) showed that both AtCNGC11l/12:G459R and AtCNGC11/12:R381H have alterations in their channel function. Site-directed mutagenesis coupled with fast-protein liquid chromatography using recombinantly expressed C-terminal peptides indicated that both mutations significantly influence subunit stoichiometry to form multimeric channels. This observation was confirmed by bimolecular fluorescence complementation in planta. Taken together, we have identified two residues that are likely important for subunit interaction for plant CNGCs and likely for animal CNGCs as well. [ABSTRACT FROM AUTHOR]

The involvement of cyclic nucleotide gated ion channels (CNGCs) in the signal transduction of animal light and odorant perception is well documented. Although plant CNGCs have recently been revealed to mediate multiple stress responses and developmental pathways, studies that aim to elucidate their structural and regulatory properties are still very much in their infancy. The structure–function relationship of plant CNGCs was investigated here by using the chimeric Arabidopsis AtCNGC11/12 gene that induces multiple defence responses in the Arabidopsis mutant constitutive expresser of PR genes 22 (cpr22) for the identification of functionally essential residues. A genetic screen for mutants that suppress cpr22-conferred phenotypes identified over 20 novel mutant alleles in AtCNGC11/12. One of these mutants, suppressor S58 possesses a single amino acid substitution, arginine 557 to cysteine, in the αC-helix of the cyclic nucleotide-binding domain (CNBD). The suppressor S58 lost all cpr22 related phenotypes, such as spontaneous cell death formation under ambient temperature conditions. However, these phenotypes were recovered at 16 °C suggesting that the stability of channel function is affected by temperature. In silico modelling and site-directed mutagenesis analyses suggest that arginine 557 in the αC-helix of the CNBD is important for channel regulation, but not for basic function. Furthermore, another suppressor mutant, S136 that lacks the entire αC-helix due to a premature stop codon, lost channel function completely. Our data presented here indicate that the αC-helix is functionally important in plant CNGCs. [ABSTRACT FROM PUBLISHER]

Context. - Resorbable substances used to achieve hemostasis during neurosurgical procedures comprise 3 principal classes based on chemical composition: (1) gelatin sponge, (2) oxidized cellulose, and (3) microfibrillar collagen. Nonresorbable hemostatic aides include various forms of cotton and rayon-based hemostats (cottonoids and kites). Resorbable and nonresorbable hemostatic agents have been reported to cause symptomatic mass lesions, most commonly following intra-abdominal surgery. Histologic examination typically shows a core of degenerating hemostatic agent surrounded by an inflammatory reaction. Each agent exhibits distinctive morphologic features that often permit specific identification. Hemostat-associated mass lesions have been variously referred to as textilomas, gossypibomas, gauzomas, or muslinomas. Objectives. - The aims of this study were to (1) identify cases of histologically proven cases of textiloma in neurosurgical operations, (2) characterize the specific hemostatic agent associated with textiloma formation, and (3) characterize the preoperative magnetic resonance imaging appearance of textiloma. Design. - Cases in which a textiloma constituted the sole finding on repeat surgery for recurrent brain tumor, or was a clinically significant component of a radiologically identified mass lesion together with residual tumor, constituted the study set. Results. - Five textilomas were identified and evaluated. The primary neoplasm was different in each case and included pituitary adenoma, tanycytic ependymoma, anaplastic astrocytoma, gliosarcoma, and oligodendroglioma. In all cases, preoperative magnetic resonance imaging suggested recurrent tumor. Textilomas included all categories of resorbable hemostatic agent. Other foreign bodies were present in some cases; the origin of these foreign bodies was traced to fibers shed from nonresorbable hemostatic material placed temporarily during surgery and removed before closure (cottonoids and kites). Inflammatory reactions included giant cells, granulomas, and fibroblastic proliferation. Microfibrillar collagen (Avitene) textilomas were associated with a striking eosinophil infiltration that was not seen with any other hemostatic agent. Conclusions. - Hemostatic agents may produce clinically symptomatic, radiologically apparent mass lesions. When considering a mass lesion arising after intracranial surgery, the differential diagnosis should include textiloma along with recurrent tumor and radiation necrosis. [ABSTRACT FROM AUTHOR]