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Rheumatoid arthritis (RA) is a chronic inflammatory disease caused by numerous genetic and environmental factors leading to musculoskeletal system pain. RA may damage other tissues and organs, causing complications that severely reduce patients’ quality of life. According to the World Health Organization (WHO), over 1.71 billion individuals worldwide had musculoskeletal problems in 2021. Rheumatologists face challenges in the early detection of RA since its symptoms are similar to other illnesses, and there is no definitive test to diagnose the disease. Accordingly, it is preferable to profit from the power of computational intelligence techniques that can identify hidden patterns to diagnose RA early. Although multiple studies were conducted to diagnose RA early, they showed unsatisfactory performance, with the highest accuracy of 87.5% using imaging data. Yet, imaging data requires diagnostic tools that are challenging to collect and examine and are more costly. Recent studies indicated that neither a blood test nor a physical finding could early confirm the diagnosis. Therefore, this study proposes a novel ensemble technique for the preemptive prediction of RA and investigates the possibility of diagnosing the disease using clinical data before the symptoms appear. Two datasets were obtained from King Fahad University Hospital (KFUH), Dammam, Saudi Arabia, including 446 patients, with 251 positive cases of RA and 195 negative cases of RA. Two experiments were conducted where the former was developed without upsampling the dataset, and the latter was carried out using an upsampled dataset. Multiple machine learning (ML) algorithms were utilized to assemble the novel voting ensemble, including support vector machine (SVM), logistic regression (LR), and adaptive boosting (Adaboost). The results indicated that clinical laboratory tests fed to the proposed voting ensemble technique could accurately diagnose RA preemptively with an accuracy, recall, and precision of 94.03%, 96.00%, and 93.51%, respectively, with 30 clinical features when utilizing the original data and sequential forward feature selection (SFFS) technique. It is concluded that deploying the proposed model in local hospitals can contribute to introducing a method that aids medical specialists in preemptively diagnosing RA and stopping or delaying the course using clinical laboratory tests.

Alzheimer’s Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to name a few. AD increases exponentially with aging, where it doubles every 5-6 years, causing profound implications, such as swallowing difficulties and losing the ability to speak before death. According to the Ministry of Health in Saudi Arabia, AD patients will triple by 2060 to reach 14 million patients worldwide. The rapid rise of patients is caused by the silent progress of the disease, leading to late diagnosis as the symptoms will not be distinguished from normal aging affect. Moreover, with the current medical capabilities, it is impossible to confirm AD with 100% certainty via specific medical examinations. The literature review revealed that most recent publications used images to diagnose AD, which is insufficient for local hospitals with limited imaging capabilities. Other studies that used clinical and demographical data failed to achieve adequate results. Consequently, this study aims to preemptively predict AD in Saudi Arabia by employing machine learning (ML) techniques. The dataset was acquired from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia, containing standard clinical tests for 152 patients. Four ML algorithms, namely, support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost), were employed to preemptively diagnose the disease. The empirical results demonstrated the robustness of SVM in the pre-emptive diagnosis of AD with accuracy, precision, recall, and area under the receiver operating characteristics (AUROC) of 95.56%, 94.70%, 97.78%, and 0.97, respectively, with 13 features after applying the sequential forward feature selection technique. This model can assist the medical staff in controlling the progression of the disease at low costs.

The present article aims to review and evaluate the practiced and classical techniques, tools, models, and systems concerning automatic information extraction (IE) from published scientific documents like research articles, patents, theses, technical reports, and case studies etc. IE is performed for various reasons such as better indexing, archiving, searching, and retrieving. That is mainly used by the search engines and the indexing services as well the digital libraries and semantic web. In this regard, several studies have been conducted targeting various nature of documents. The study pays special consideration to the successful IE models, algorithms and approaches applied to structural IE from published documents. To grasp this, the paper is classified into several segments and each segment covers a significant aspect of IE. Furthermore, to validate their benefits and drawbacks, a comparative study of all the approaches have been conducted in terms of various performance factors like precision, accuracy, recall and F-score. Potential areas of improvement have been emphasized as research gap for the scholars in the closely related areas. Ultimately, a comprehensive summary of the evaluation is presented in tabular form and review is concluded. It was observed that the hybrid methods outperform the other methods due to their versatile nature to address various document formats.

Supplementary Figures S1-S3; Supplementary Table S1.

Purpose: Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in childhood. The alveolar subtype of rhabdomyosarcoma (ARMS) is a paradigm for refractory and incurable solid tumors because more than half of the children at diagnosis have either regional lymph node or distant metastases. These studies follow our previous observation that Interleukin-4 receptor α (IL-4Rα) is upregulated in both human and murine ARMS, and that the IL-4R signaling pathway may be a target for abrogating tumor progression.Experimental Design: By in vitro biochemical and cell biology studies as well as preclinical studies using a genetically engineered mouse model, we evaluated the role of IL-4 and IL-13 in IL-4R–mediated mitogenesis, myodifferentiation, and tumor progression.Results: IL-4 and IL-13 ligands accelerated tumor cell growth and activated STAT6, Akt, or MAPK signaling pathways in the human RMS cell lines, RD and Rh30, as well as in mouse primary ARMS cell cultures. IL-4 and IL-13 treatment also decreased protein expression of myogenic differentiation factors MyoD and Myogenin, indicating a loss of muscle differentiation. Using a genetically engineered mouse model of ARMS, we have shown that inhibition of IL-4R signaling pathway with a neutralizing antibody has a profound effect on the frequency of lymph node and pulmonary metastases, resulting in significant survival extension in vivo.Conclusions: Our results indicate that an IL-4R-dependent signaling pathway regulates tumor cell progression in RMS, and inhibition of this pathway could be a promising adjuvant therapeutic approach. Clin Cancer Res; 17(9); 2757–66. ©2011 AACR.

On the telecommunications front, 5G is the fifth-generation technology standard for broadband cellular networks, which is a replacement for the 4G networks used by most current phones. Hundreds of businesses, organizations, and governments suffer from cyberattacks that compromise sensitive information in which 5G is one of them. Those breaches of the data would not have occurred if there is a way to detect strange behaviors in a 5G network, and this is what this paper presenting. Network Anomaly Detection (NAD) in 5G is a way to observe the network constantly to detect any unusual behavior. However, it is not that straightforward and rather a complex process due to huge, continuous, and stochastic network traffic patterns. In the literature, several approaches and methods have been employed for anomaly detection as well as prediction. This paper illustrates state-of-the-art method to proposed achieve the NAD. For instance, pattern based, machine learning based, ensemble learning based, user intention based, and some integrated methods have been surveyed and analyzed. KNN and K-prototype algorithm were tested together on the dataset and compared with integrated approach. The integrated approach outperformed with respect to the KNN and K-prototype methods. As a conclusion, forecasting of analyst detection of cyber events is presented as a final method for future anomaly prediction.

Purpose: The study was conducted to diagnose transient tachypnoea of the newborn (TTN) in the early stage using ultrasonography and to estimate the sensitivity and specificity of double lung point (DLP) sign in diagnosing TTN. Material and methods: The study population included premature neonates with respiratory distress, admitted in the neonatal intensive care unit from December 2017 to June 2019, who fulfilled the inclusion criteria. A total of 100 patients were included in the study, and they underwent lung ultrasonography within 6 hours of birth. Inclusion criteria were as follows: preterm babies born < 37 weeks of gestation presenting with respiratory distress within 6 hours, clinically diagnosed with TTN and other causes like respiratory distress syndrome and pneumonia. Term neonates and neonates with congenital malformations and trauma were excluded from the study. Preterms with respiratory distress were enrolled in the study. Based on the clinical findings and laboratory investigations, clinical diagnosis was made by the paediatrician. After obtaining informed consent, ultrasonography of bilateral chest was performed using a Philips HD7 XE and a Sonoscape S2 portable ultrasound machine with a linear transducer (6-12 MHz) within 6 hours of birth. Results: The mean gestational age was 33.0 ± 1.9 weeks. Double lung point sign was present in 55 preterm neonates in our study. It was most common in bilateral posterior lung fields. The sensitivity and specificity of DLP in diagnosing TTN was 85% and 100%, respectively. Conclusions: It was found in our study that double lung point sign is the primary ultrasonographic characteristic of TTN, and ultrasonography can almost accurately diagnose TTN in early stages.

India has the world's second-largest population and the demand for transportation is increasing. However, our energy resources for car engines are limited. Every day, India consumed over 4.9 million barrels of crude oil. 85 percent of total oil consumption is imported from nations such as Iran, Saudi Arabia and Russia, among others. India is ranked third in the world. Despite our efforts to transition from internal combustion to electric power, India's electricity is heavily reliant on coal, which accounts for 55 percent of total production. As a result of this circumstance, jatropha biodiesel is being sought as a diesel engine alternative fuel. According to the Indian government, the country will spend about Rs 8 lakh crores on diesel and petrol imports in 2021, with this figure expected to rise to Rs 25 lakh crores in the next five years. The features of jatropha biodiesel with various percentages of blending with diesel fuel, such as B0, B5, B10, B15, B20, B30, B40, B50 and B100 are investigated in this work.

The undergoing research aims to address the problem of COVID-19 which has turned out to be a global pandemic. Despite developing some successful vaccines, the pace has not overcome so far. Several studies have been proposed in the literature in this regard, the present study is unique in terms of its dynamic nature to adapt the rules by reconfigurable fuzzy membership function. Based on patient’s symptoms (fever, dry cough etc.) and history related to travelling, diseases/medications and interactions with confirmed patients, the proposed dynamic fuzzy rule-based system (FRBS) identifies the presence/absence of the disease. This can greatly help the healthcare professionals as well as laymen in terms of disease identification. The main motivation of this paper is to reduce the pressure on the health services due to frequent test assessment requests, in which patients can do the test anytime without the need to make reservations. The main findings are that there is a relationship between the disease and the symptoms in which some symptoms can indicate the probability of the presence of the disease such as high difficulty of breathing, cough, sore throat, and so many more. By knowing the common symptoms, we developed membership functions for these symptoms, and a model generated to distinguish between infected and non-infected people with the help of survey data collected. The model gave an accuracy of 88.78%, precision of 72.22%, sensitivity of 68.42%, specificity of 93.67%, and an f1-score of 69.28%.

Two-dimensional (2D) nanoflakes of vanadium pentoxide (V2O5) are directly developed on the titanium (Ti) substrate (VO@Ti) as an innovative electrode for pseudocapacitor. The designed VO@Ti electrode offers impressive capacitance of 1520 Fg-1 at 1.5 Ag-1 and superb long-term reliability with a 99% retention over 12000 cycles at 20 Ag-1 based on the merits of highly exposed active sites and improved electrical conductivity.

In the era of cloud computing, face recognition, one of the most useful applications of image processing, is essential. Human face recognition is a key issue for verification, especially when it comes to tracking student attendance. The objective of this work is to implement an image processing-based automated system for monitoring attendance to both capture the photos and store the dataset. This classroom camera system recognises faces and logs the information in an excel spreadsheet.

In this study, a real-time sign and emotion identification system is developed utilising a deep learning toolkit for sign language and a traditional neural network for emotion recognition.We aim to combine a manual sign language recognition system with an emotion recognition system that can recognise both emotions and gestures in real time because sign language is not an international language and cannot be used as a medium of communication between an impaired person and a normal person.

The 0.5 and 1 mol% thiourea “mixed” potassium dihydrogen phosphate (KDP) crystals have been developed by conventional slow solution evaporation method. The crystallographic parameters of grown crystals have been determined by employing single crystal X-ray diffraction technique. The functional groups of grown crystals were successfully identified by means of FTIR spectral analysis. The optical transmittance is 79%, 84%, and 89% for KDP, 0.5 mol thiourea mixed KDP, and 1 mol thiourea mixed KDP crystal. The energy band gap (Eg) of KDP, 0.5 mol thiourea mixed KDP, and 1 mol thiourea mixed KDP crystal is 3.71 eV, 3.61 eV, and 3.75 eV, respectively. The Kurtz–Perry test has been employed to determine the SHG efficiency and SHG efficiency of 0.5 and 1 mol thiourea mixed KDP crystal is 2.09 and 2.22 times superior to KDP crystal. Effect of thiourea mixing on hardness properties of KDP crystal have been scrutinized using the Vickers microhardness studies. The frequency dependent dielectric behavior of grown crystals has been analyzed at room temperature.

Nowadays we are able to track vehicles using many applications which help in securing personal vehicles, public vehicles, feet units and others. Furthermore there is a rapid increase in the occurrence of the Road accident. This project is about a system which is developed to automatically detect an accident and alert the nearest hospitals and medical services about it. This system can also locate the place of the accident so that the medical services can be directed immediately towards it. The goal of this paper is to build up a Vehicle accidental monitoring system using MEMS, GPS and GSM Technology. The system comprises of accelerometer, MCU, GPS & GSM Module support in sending message. The accelerometer is used to detect fall and Threshold Algorithm are used to detect accident. Short Message will contain GPS [Latitude, Longitude] which helps in locating the vehicles.

Global food security and sustainable agriculture are two major concerns for escalating human population and the rise in food demands piles up the usage of chemical fertilizers and pesticides. This drastically affects microbial diversity, deteriorates soil fertility, and causes ecological imbalance. A significant and substantial amount of functional N fixing bacterial community inside and in close vicinity of roots supply N efficiently in irrigated ecosystems and their diversity can be altered by organic and inorganic inputs in wetland rice cultivation system. The study explored the existence of culturable diazotrophs bacterial diversity in rice-grown soils those were non fertilized and differently fertilized at Rajendranagar farm fields of ICAR-Indian Institute of Rice Research (IIRR), Hyderabad, India. The bacterial isolates were identified by 16SrRNA, and the isolated species of bacterial genera Microbacterium, Mesobacillus and Brevundimonas were not reported earlier in the rice cultivation system. Each of these bacterial isolates is capable of synthesising indole-3-acetic acid (IAA), ammonia and aminocyclopropane-1carboxylate deaminases, 90 percent of the bacterial isolates can produce amylase, 80 per cent have antifungal activity, 70% were diazotrophs and varied percentages of bacterial isolates reported other essential PGP traits. Significant improvement in seedling growth with the application of bacterial isolates was observed. Five of the ten bacterial isolates improved root length, shoot length, total biomass, and seed vigour index by 11–63%, 18–38%, 4–68%, and 20–56% respectively, compared with the control (sterile water) in sterile 0.7% water agar.

Epilepsy is the second most common neurological disease after Alzheimer. It is a disorder of the brain which results in recurrent seizures. Though the epilepsy in general is considered as a serious disorder, its effects in children are rather dangerous. It is mainly because it reasons a slower rate of development and a failure to improve certain skills among such children. Seizures are the most common symptom of epilepsy. As a regular medical procedure, the specialists record brain activity using an electroencephalogram (EEG) to observe epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate and depend on the specialist's experience; therefore, automated detection of epileptic pediatric seizures might be an optimal solution. In this regard, several techniques have been investigated in the literature. This research aims to review the approaches to pediatric epilepsy seizures' identification especially those based on machine learning, in addition to the techniques applied on the CHB-MIT scalp EEG database of epileptic pediatric signals.

India is the second largest producer of fruits and vegetables in the world after China. Since 1991, the global export of fruits and vegetables has improved rapidly. The number of commodities as well as the number of varieties produced and traded have increased manifold during the past 30 years. There is an overall increase in the demand for fruits and vegetables for consumption in the fresh and processed form. Also there is a wide diversification in production pattern globally. In spite of being one of the largest producers of fruits and vegetables in the world, the export share of India is very low. The food processing industry in India is one of the largest in terms of production, consumption, export and growth prospects. However, only 2.2 percent of fruits and vegetables are processed in India against 83 percent in Malaysia, 30 percent in Thailand, 60-70 percent in United Kingdom and 70 percent Brazil. Therefore, the production of fruits and vegetables has been remarkable but its export share is very low / negligible and processing of fruits and vegetables is only 2.2 percent., {"references":["1.\tAbadias M, Usall J, Anguera M, Solsona C, Viñas I. Microbiological quality of fresh, minimally-processed fruit and vegetables, and sprouts from retail establishments. International journal of food microbiology. 2008; 123(1-2):121-9. 2.\tAchtman M, Wain J, Weill F-X, Nair S, Zhou Z, Sangal V, et al. Multilocus sequence typing as a replacement for serotyping in Salmonella enterica. PLOS pathogens. 2012; 8 (6):e1002776. 3.\tAdebayo-Tayo B, Odu N, Esen C, Okonko I. Microorganisms associated with spoilage of stored vegetables in Uyo Metropolis, Akwa Ibom State, Nigeria. Nature and Science. 2012;10 (3):23-32. 4.\tAlali WQ, Mann DA, Beuchat LR. Viability of Salmonella and Listeria monocytogenes in delicatessen salads and hummus as affected by sodium content and storage temperature. Journal of food protection. 2012; 75(6):1043-56. 5.\tÁlvarez-Ordóñez A, Fernández A, Bernardo A, López M. Acid tolerance in Salmonella typhimurium induced by culturing in the presence of organic acids at different growth temperatures. Food Microbiology. 2010;27(1):44-9."]}

Exports have played an increasingly important role in India’s economic growth in the last two decades. This paper analyses the performance of India’s exports and the various economic factors which have contributed to its growth. The study about which commodity will highly export from India to other countries., {"references":["1.\tAbed, F.H. [1995]: Handicraft Development and Marketing in Bangladesh, Souvenir on National Craft Show-'95, June 7-8, Jointly Organised by the EPB and Bangla craft, Dhaka. 2.\tADB/INFOFISH [1991]: Global Industry Update--Shrimp, Kuala Lumpur, Malaysia, August. 3.\tAhmed, A.H.M. Mahfuzzudin [1985]: Economic Coastal Shrimp Culture in a Mixed Farming System - A Study on Chittagong Cox's Bazar Reigon in Bangladesh, Unpublished MS. Dissertation, University Pertanian Malaysia, Serdarng, Selanger, Malaysia. 4.\tAhmed, Faruquddin and Khan, Syed Ahmed [1996]: Review of Exports from Bangladesh, Paper presentation in the Seminar on \"Development and Finance of Exports of Bangladesh\" at Bangladesh Bank, April 15-16. 5.\tAhsan, Ullah [1995]: Export of Fresh and Processed Vegetables from Bangladesh, Paper presented in the Workshop on Vegetables Crops Agribusiness, Sponsored by BARI-AVRDC-USAJD, May 2-4"]}

The development of smart sensors towards monitoring health and environmental has been receiving growing attention in the recent past. In particular, development of wearable, flexible, stretchable, and semitransparent sensors are desirable for modern technological applications. In this work, we present a simple strategy towards preparation of highly flexible and stretchable composite sensors based on poly (3,4-ethylenedioxythiophene: poly(styrene sulfonic acid)) (p-PEDOT–PSS) and titanium dioxide (n-TiO2) treated with diethylene Glycol (DEG) using a simple bar coating technique. The surface morphology, distribution of TiO2 nanoparticles, thermal stability, and various functional groups present in the composite were characterized by SEM, TEM, TGA, and FT-IR techniques. Presence of TiO2 and DEG treatment in PEDOT–PSS enhances the electrical conductivity (2 S/cm to 515 S/cm) and mechanical stability of the composite films. This ultra-sensitive, highly flexible, and stretchable thin layer of PEDOT–PSS:TiO2 treated with (DEG) was employed as a resistive sensor for the detection of Nitric Oxide (NO) (1–250 ppm) at room temperature. These ultra-sensitive nanocomposite films exhibit a sensitivity of 96% (@ 250 ppm) with excellent sensitivity retention under mechanical deformations such as bending and stretching. For the first time, this report presents the simultaneous determination of conductivity and NO-sensing performance of rolled and stretched sensing films based on conductive PEDOT–PSS composites. Owing to its low-cost fabrication, excellent conductivity, high sensitivity under mechanical deformations and long-term stability, the composite films studied in this work could potentially be used as a wearable and flexible gas sensor in organic electronics towards detection of NO gas.

Islamic banking is one of the fastest developing banking systems in the financial sector, with vast opportunities worldwide. Islamic banking operates by focussing on complete transparency, ethical investment avenues, co-operative businesses, risk sharing that attract not only the followers of Islam but people from other faiths also. Considering the growth Internationally–Islamic banking has become one of the fastest-growing segments in the International markets as well as Capital markets. With around 195 million Muslims approximately, India’s Muslim population is about the world’s third-largest and the world’s largest Muslim-minority population. India is home to 10.3% of the world’s Muslim population. Islamic banking system operates strictly by the Shariah laws which prohibit collection or payment of Interest, known as “Riba”. The Islamic banking system also prohibits investments in businesses or ventures that are considered Unlawful or Haraam. Risk bearing is one of the most basic principles based on which the Islamic banking system works, which relates to trading rather than risk transfer as in the case of conventional banking. The research papers aim at explaining the services of Islamic banking and various principles of Islamic banking. The research also intends to put forward the benefits and drawbacks of Islamic banking in the Indian context.

Hollow Glass Microspheres (HGM) is now challenging reinforcement for making low density composites with high stiffness, good electrical and mechanical properties. Carbon nano tubes when reinforced homogenously in the matrix it enhance properties of the composites to a commendable range. These CNTs are grown over HGM using chemical vapor deposition (CVD) technique at 600 °C temperature. Initially HGM were nickel coated by electroless deposition technique using nickel sulphate as the source of metal in the alkaline bath and then the catalyst coated HGMs were exposed to the mixture of acetylene and nitrogen gas at various temperatures to form CNT over HGM. Scanning Electron Microscopy (SEM) images explore the quality and dimensions of CNT. Other characterizations such as XRD, Raman Spectroscopy confirm the presence of CNT. Using these coated HGM as fillers in Epoxy, composites were fabricated and the same was characterized for compression using UTM and DMA using DMA analyzer.

The objective of this study is to examine the impact of sectoral shift on the stock return of Bangladesh. This study employs auto-regressive distributive lag (ARDL) approach using the weekly data of various sectoral indices of Bangladesh over the period from May 1999 to September 2016. The findings tend to indicate that there has possible sectoral portfolio diversification in the market and ‘general product industry' is the most exogenous and profitable sector from the rest. This study is one of the first attempts of the sectoral analysis and its impact on the stock return with the reference to Bangladesh. Furthermore, this study can be a benchmark for the policymakers of emerging economies to find the impact of economic transformation in the stock returns of the equity markets.

Polyurethanes (PU) are versatile polymers which can be molded to any shape. Reinforcing suitable micro and nano particles in the PU foam enhances its comfort, warmth and convenience to the consumer products. PU foams have versatile application due to its commendable comfort and insulation properties. Challenge for the foam is its strength, in the present work strength of PU foam is increased by reinforcing alumina to it. For the weight fraction of 1–35% of alumina, maximum increase in tensile strength and modulus of 35% was obtained. X-ray diffraction, SEM analysis and sol fraction of the composites were also carried out.

Introduction: Medical devices are boon to the healthcare system and are available in the market since long ago. More than 500,000 different types of the medical devices are available in the international market. Thus, from the patient safety view point, assessment of the quality and safety of these medical devices is essential. Objectives: This review article discusses the classification and regulation of medical devices in India and the world with framework of adverse event reporting system for medical devices in India. Methods: To address the aforesaid issue International Medical Device Regulators Forum (IMDF) was established in 2011 was established at international Level. In India, 4 years later in 2015, Materiovoigilance Program of india (MvPI) was introduced with the prime aim of improving the protection of the health and safety of patients, healthcare professionals and others by reducing the likelihood of reoccurrence of an adverse event associated with the use of medical devices. Results: At present, there are 50 Medical Device Adverse Event Monitoring Centres (MDMCs) in India. Every country has its own regulatory body and guidelines for monitoring and reporting of adverse events due to medical devices eg: USFDA in USA, TGA in Australia, MHRA in UK, ENVISA in Brazil, CDSCO in India etc. Conclusions: In India, the provisions of regulation of safety, quality and performance of medical devices are laid down in the Drugs and Cosmetics Act, 1940 and rules 1945.

To assess the adverse effects of propranolol therapy in infantile hemangioma. An ambispective study was conducted from August 2011 to December 2019. In retrospective arm all children managed for infantile hemangioma with propranolol were included and case records were assessed for adverse reactions. In prospective arm the adverse reactions were identified on the basis of predefined criteria. A total of 514 patients (358 retrospective records) were included. A majority, 378 (73.5%) patients had an excellent response, 75 (14.5%) had partial response and 61 (11.8 %) had no response. A total of 82 (15.9%) patients experienced at least one adverse effect. Diarrhea with weight loss (27, 32.9%) and irritability with decreased sleep (21, 25.6%) were the most common adverse effects. The adverse effects in 22 (4.2%) cases lead to the discontinuation of propranolol. Younger age, low body weight and early onset were risk factors for development of severe adverse reactions. Young children with low body weight were at higher risk for adverse effects of propranolol.

At present world is lurching under the spread of new SARS-CoV-2 infection The treatment is still elusive despite the relentless effort by the scientists against various viral structures Whereas the 3-Chymotrypsin-like proteases cleave polyproteins and structural proteins help in viral replication At the same time, non-structural proteins stimulate mRNA cap methylation to evade the immune response The present study aims to identify novel dual inhibitor compounds with potential hits simultaneously against any of these three targets, including 3C-like proteases, N-protein, and NSP16 through virtual screening, molecular docking approach, and molecular dynamics Such dual inhibitors may provide the necessary treatment to alleviate the current pandemic We screened 265 FDA approved infectious disease drugs against three types of Covid-19 targets, i e , 3C-like proteinase (6w63), N-protein (6vyo), and Non-structural protein 16 (6w4h) using a computer-assisted drug repurposing approach in this study The Schrodinger suite 2019 is employed for high throughput screening, molecular docking, and binding free energy through the Glide module We sorted 27 drugs, out of which the best common three drugs were suggested based on their virtual statistics parameters We found three drugs that belonged to two main categories as dual inhibitors The Plazomicin (Aminoglycoside) and Cefiderocol (Cephalosporins) are an antibiotic group of drugs, and the Vanganciclovir is antiviral The molecular dynamics simulation studies over 30000ps for Plazomicin against NSP16 was conducted based on their promising docking scores profile The RMSD parameter remained stable at 2 5A for ligands for 30000ps Thus these three compounds can be validated as a SARS-CoV-2 therapy through clinical trials © 2020 by the authors

Addition of fillers in polyurethane foams enhances the mechanical properties of polymeric foams. However, fillers can be loaded to a certain extent, as higher percentage of fillers in polymeric foam causes structural instability leading to the collapse of foam. In this article, we report the use of hollow glass microspheres as a possible co-filler which enables higher loading of nano-clay in flexible polyurethane foam. It has been observed that the structural and mechanical properties of nano-clay-loaded foams were found to cause instability at 5 wt% loading of nano-clay. Therefore, upon addition of hollow glass microspheres in 5 wt%, nano-clay-loaded polyurethane foam shows remarkable enhancement in terms of stability and mechanical properties of the resultant foams. A 100-fold increment in tensile strength has been observed for 2 wt% hollow glass microspheres and 5 wt% nano-clay-loaded flexible polyurethane foams as compared to conventional (unloaded) polyurethane foams.

A three-layered composite wipe was fabricated by laminating individual layers of non-woven polypropylene, activated carbon fabric (ACF) and aramid fabric for the sampling and investigation of chemical warfare agents (CWA)-contaminated urban porous and non-porous surfaces. The material of main ACF layer was characterized to ascertain its suitability to act as an efficient adsorbent for the surface wipe sampling. The performance of ACF-based composite wipe was determined by evaluating its extraction efficiency, wiping efficacy and adsorption capacity for the sampling of blister and nerve agent class of CWA-contaminated surfaces using gas chromatography-mass spectrometry (GC-MS). Parameters like amount of wipe required, solvent selection, amount of solvent, time of extraction etc. were optimized to achieve the maximum recovery of contaminating analytes required for the forensic investigations. Overall recoveries of contaminating analytes after sampling and extraction were found to be in the range of 45–85% for all types of surfaces. No breakthrough in wiping process was noticed up to contamination density (CD) 1.6 mg/cm2 for non-porous surface and 3.2 mg/cm2 for porous surfaces. ACF-based wipe was found capable to significantly reduce the vapour hazards from liquid sulphur mustard (HD) and sarin (GB). Contamination from surfaces could be preserved within the wipe up to 15 days for the extended forensic investigation purposes. Limit of detections (LOD) of contaminants was determined in the range of 0.8–6.8 ng/cm2 while limit of quantitation (LOQ) was achieved up to the range of 2.4–14.4 ng/cm2 for wipe sampling of different surfaces.

In the present work kyanite (Al2SiO5) used as reinforcement material by varying weight-percentage of 2 wt%, 4 wt%, 6 wt% and 8 wt% in Al7075 alloy matrix material. Al7075-kyanite composites prepared using vortex method by stir casting technique. Microstructural studies of cast composites is carried out using optical microscopy and X-ray diffraction studies. XRD studies shows the peaks subsequent to Al, Al2SiO5 and minor Al2O3, SiO2 phases. Optical microscopy results the uniform distribution of kyanite in Al7075 matrix. The dry pin-on-disc unlubricated wear tests conducted to observe the wear behaviour of the Al7075-alloy and its casted composites. The dry sliding wear tests carried out at various sliding speeds (0.6, 0.8 and 1 m/s), sliding distances (250, 500, 750 and 1000 m) and loads (20, 40 and 60 N). It is contingent that, wear rate of Al-Al2SiO5 composite is lesser than unreinforced Al 7075 in all combination of sliding speeds, sliding distances and loads. The wear rate is decreases 9.9 × 10−4 ± 6.1 × 10−4 mm3/m increase in wt% of kyanite in matrix material and sliding speed. Wear rate is decreases 7.19 × 10−4 ± 1.71 × 10−4 mm3/m increasing sliding distance. Wear rate is increases 1.52 × 10−3 ± 0.3 × 10−3 mm3/m increasing load. Brinell hardness tested increases hardness with increase in wt% of kyanite in matrix. Worn surface analysis is carried out to analyze the effect of reinforcement on worn surfaces. Hardness is superior for Al7075/8 wt% kyanite composites. Composites gives higher hardness and wear resistance compared to Al7075 matrix.

Products made of syntactic foams are replacing many conventional materials due to several desirable properties and commendable low density. To overcome its limitation in application it has been hybridized with CNT in this work to achieve multifunctional, mechanical and thermo-mechanical enhanced composites. It is found that when HGM is added to epoxy/CNT matrix compressive strength increases till 5% to a maximum value of 176MPa at 5wt% of HGM and beyond this volume of HGM strength decreases considerably. Storage modulus increases with increase in HGM volume due to increased restriction in polymer chain mobility of epoxy matrix. Microstructure images of fractured surface of the composites evident the good dispersion of HGM and CNT in the epoxy matrix. It can also be seen in SEM images that many microcracks generated are discontinuous and hence increasing the stability of composite in plastic region