Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model.

Microgrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.

FOPBIE: Multi-image cipher based on the random walk of fleet of pawns on the large hypothetical chessboard and chaotic system.

In the last two decades or so, a large number of image ciphers have been written. The majority of these ciphers encrypt only one image at a time. Few image ciphers were written which could encrypt multiple images in one session. The current era needs speedy multiple image ciphers to address its varied needs in different settings. Motivated by this dictation, the current study has ventured to write a multi-image cipher based on the fleet of pawns walking in the large hypothetical chessboard. This walk of pawns on the chessboard has been ingeniously linked with transferring the pixels from the plain image to the scrambled image. The confusion effects have been realized through the XOR operation between the scrambled image and the key image. The plaintext sensitivity has been incorporated by embedding the SHA-384 hash codes of the given large combined plain image. Moreover, the Henon map has been employed to spawn the streams of random numbers. Besides, Blum Blum Shub random number generator has been used to further cement the security of the proposed cipher. We got a computational time of 0.2278 seconds and an encryption throughput of 5.5782 MBit/seconds by using the four images with a size of 256×256. Apart from that, the information entropy gained is 7.9993. Lastly, the cipher has been subjected to an array of validation metrics to demonstrate its aversion to the myriad threats from the cryptanalysis savvy. We contend that the proposed work has great potential for some real-world applications.

Exploring quality of life, discrimination, and knowledge of parents of ADHD children in Saudi Arabia: A cross-sectional study.

Attention-deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in childhood. This study investigates the quality of life (QoL) of parents of children with ADHD, the discrimination they encounter, and their sources of information about the disorder. This cross-sectional study was conducted using an online questionnaire through Google Forms from March to April 2023. Study participants were recruited using a convenient sampling technique from patient records in 4 regions of Saudi Arabia. The questionnaire was sent through commonly used social media. QoL was assessed through the validated Arabic version of the World Health Organization Quality of Life Instrument, Short Form questionnaire. A total of 100 participants were recruited for this study. Most of the participants were from the southern region (80%, n = 80), 66.0% (n = 66) were mothers, 66.0% (n = 66) were married and 64% (n = 64) had a college degree. The mean scores of the physical, psychological, mental, environmental, total scales, and general QoL were (57.18 ±â€…13.67, 62.58 ±â€…17.49, 63.33 ±â€…23.12, 23.07 ±â€…12.87, 51.54 ±â€…14.34, 3.69 ±â€…1.06, respectively). Half of the population studied experienced discrimination with no significant differences between mothers and fathers (64% vs 68%, P = .833). The main source of information on ADHD was the Internet (49%) followed by schools (11%), and relatives (10%). Fifty-four percent of the participants have participated in workshops or seminars on neurodevelopmental disorders and 39.0% believed that schools and institutions are sufficiently equipped to support children with ADHD. ADHD had a profound impact on the QoL of parents, with a substantial portion facing discrimination due to their child condition. Additionally, parents showed a strong desire to acquire more information about ADHD, highlighting the need for a comprehensive understanding and support surrounding this condition.

Aflatoxins in Peanut (Arachis hypogaea): Prevalence, Global Health Concern, and Management from an Innovative Nanotechnology Approach: A Mechanistic Repertoire and Future Direction.

Arachis hypogaea is the most significant oilseed nutritious legume crop in agricultural trade across the world. It is recognized as a valued crop for its contributions to nourishing food, as a cooking oil, and for meeting the protein needs of people who are unable to afford animal protein. Currently, its production, marketability, and consumption are hindered because of Aspergillus species infection that consequently contaminates the kernels with aflatoxins. Regarding health concerns, humans and animals are affected by acute and chronic aflatoxin toxicity and millions of people are at high risk of chronic levels. Most methods used to store peanuts are traditional and serve effectively for short-term storage. Now the question for long-term storage has been raised, and this promptly finds potential approaches to the issue. It is imperative to reduce the aflatoxin levels in peanuts to a permissible level by introducing detoxifying innovations. Most of the detoxification reports mention physical, chemical, and biological techniques. However, many current approaches are impractical because of time consumption, loss of nutritional quality, or weak detoxifying efficiency. Therefore, it is crucial to investigate practical, economical, and green methods to control Aspergillus flavus that address current global food security problems. Herein, a green and economically revolutionary way is a nanotechnology that has demonstrated its potential to connect farmers to markets, elevate international marketability, improve human and animal health conditions, and enhance food quality and safety by the management of fungal diseases. Due to the antimicrobial potential of nanoparticles, they act as nanofungicides and have an incredible role in the control of aflatoxins. Nanoparticles have ultrasmall sizes and therefore penetrate the fungal body and invade the pathogen machinery, leading to fungal cell death by ROS production, mutation in DNA, disruption of organelles, and membrane leakage. This is the first mechanistic overview that unveils a comprehensive insight into aflatoxin contamination in peanuts, its prevalence, health effects, and management in addition to nanotechnological interventions that serve as a triple defense approach to detoxify aflatoxins. The optimum use of nanofungicides ensures food safety and the development of goals, especially "zero hunger".

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions.

Introduction: The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. Methods: In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. Results and discussion: To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

Eco-friendly graphene quantum dots as a novel spectrofluorimetric probe for lamivudine quantification with evaluation of its greenness and blueness profiles.

In this study, graphene quantum dots (GQDs) were employed for quantitatively analyzing lamivudine using a fluorescence quenching technique. This approach allows for sensitive determination of the concentration of lamivudine in different matrices without requiring derivatization. The mechanism behind the fluorescence intensity quenching between GQDs and lamivudine molecules was explored using the Stern Volmer equation, revealing dynamic quenching behavior. Additionally, various factors affecting fluorescence quenching efficiency such as pH, GQDs concentration, and incubation time were carefully tuned. Moreover, our developed method successfully met ICH guidelines for validation parameters including linearity, accuracy, precision, and selectivity demonstrating excellent performance. The results showed good accuracy and precision, with a mean recovery value of 101.91% for method accuracy and a relative standard deviation of 0.682 and 1.489 for intraday and interday precision, respectively. Finally, the greenness and blueness of the developed method were also investigated to assess its environmental friendliness and analytical practicality. Greenness evaluation using the AGREE tool demonstrated that the developed method has a low environmental impact with an AGREE score of 0.75, Besides, the blueness evaluating using the BAGI tool indicated that the developed method is practical, reliable, and well-suited for routine analysis of lamivudine in various samples.

Utilization of absorbance subtraction and ratio difference green spectrophotometric methods for the quantification of alfuzosin hydrochloride and tadalafil in their binary mixture.

Alfuzosin hydrochloride and tadalafil fixed-dose combination tablets were recently formulated for the treatment of individuals with lower urinary tract symptoms caused by benign prostatic hyperplasia. Herein, the first spectrophotometric methods for quantitative analysis of alfuzosin hydrochloride and tadalafil in their binary mixture were established. The spectral overlapping of alfuzosin hydrochloride and tadalafil made direct simultaneous analysis unfeasible. Therefore, two mathematical methods were used to solve these overlapping spectra: absorbance subtraction and ratio difference. The absorbance subtraction method manipulates the zero absorption spectra of the studied drugs at the isoabsorptive point (272 nm) and uses the absorbance factor of pure ALF to calculate the absorbance of the studied drugs in the mixture at the isoabsorptive point. The ratio spectra method, on the other hand, manipulates the ratio spectra of the studied drugs, which are obtained by dividing each drug's zero absorption spectra by a divisor spectrum from the second drug. The ratio amplitude difference between 251 nm and 211 nm was directly proportional to alfuzosin hydrochloride, whereas between 292 nm and 222 nm it was directly proportional to tadalafil. The methods used were verified in accordance with the recommendations of the ICH and demonstrated adequate linear regression in working ranges of 1-15 µg/mL for alfuzosin hydrochloride and 3-40 µg/mL for tadalafil. The methods were accurate, precise, and selectively employed to quantify alfuzosin hydrochloride and tadalafil in their combined tablets.

Heart patient health monitoring system using invasive and non-invasive measurement.

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professionals typically use electrocardiogram (ECG) signals and certain preliminary tests to identify abnormal patterns in a patient's cardiac activity. To assess the overall cardiac health condition, cardiac specialists monitor these activities separately. This procedure may be arduous and time-intensive, potentially impacting the patient's well-being. This study automates and introduces a novel solution for predicting the cardiac health conditions, specifically identifying cardiac morbidity and arrhythmia in patients by using invasive and non-invasive measurements. The experimental analyses conducted in medical studies entail extremely sensitive data and any partial or biased diagnoses in this field are deemed unacceptable. Therefore, this research aims to introduce a new concept of determining the uncertainty level of machine learning algorithms using information entropy. To assess the effectiveness of machine learning algorithms information entropy can be considered as a unique performance evaluator of the machine learning algorithm which is not selected previously any studies within the realm of bio-computational research. This experiment was conducted on arrhythmia and heart disease datasets collected from Massachusetts Institute of Technology-Berth Israel Hospital-arrhythmia (DB-1) and Cleveland Heart Disease (DB-2), respectively. Our framework consists of four significant steps: 1) Data acquisition, 2) Feature preprocessing approach, 3) Implementation of learning algorithms, and 4) Information Entropy. The results demonstrate the average performance in terms of accuracy achieved by the classification algorithms: Neural Network (NN) achieved 99.74%, K-Nearest Neighbor (KNN) 98.98%, Support Vector Machine (SVM) 99.37%, Random Forest (RF) 99.76 % and Naïve Bayes (NB) 98.66% respectively. We believe that this study paves the way for further research, offering a framework for identifying cardiac health conditions through machine learning techniques.

A nanocomposite competent to overcome solubility and permeation issues of capsaicin and thiocolchicoside simultaneously in gout management: Fabrication of nanocubosomes.

This study aimed to formulate nano-cubosomes (NCs) co-loaded with capsaicin (CAP) and thiocolchicoside (TCS) to enhance their bioavailability and minimize associated potential side effects through transdermal delivery alongside their synergistic activity. Twenty seven (27) nano-cubosomal dispersions were prepared according to Box-Behnken factorial design and the effect of CAP, TCS, glyceryl mono oleate (GMO) and poloxamer 407 (P407) concentrations on particle size, polydispersity index (PDI), zeta potential, and entrapment efficiency were assessed. The results revealed that the optimized formulation exhibited a mean droplet size of 503 ± 10.3 nm, PDI of 0.405 ± 0.02, zeta potential of -10.0 ± 1.70 mV and entrapment efficiency of 86.9 ± 3.56 %. The in vivo anti-inflammatory effect of optimized formulation was studied in rats by injecting carrageenan to induce edema. The results of in vivo study showed that transdermal application of nano-cubosomes co-loaded with CAP and TCS significantly (p value < 0.05) improved carrageenan induced inflammation compared with standard treatment. The analgesic activity of optimized formulation was evaluated in rats by using Eddy's hot plate method. The findings of analgesic activity illustrated that the analgesic effects exhibited by test formulation may be associated with increased licking period and inhibition of prostaglandins level. In conclusion, the transdermal application of NCs co-loaded with CAP and TCS may be a promising delivery system for enhancing their bioavailability as well as synergistic analgesic and anti-inflammatory activity in gout management.

LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest.

Sign language recognition presents significant challenges due to the intricate nature of hand gestures and the necessity to capture fine-grained details. In response to these challenges, a novel approach is proposed-Lightweight Attentive VGG16 with Random Forest (LAVRF) model. LAVRF introduces a refined adaptation of the VGG16 model integrated with attention modules, complemented by a Random Forest classifier. By streamlining the VGG16 architecture, the Lightweight Attentive VGG16 effectively manages complexity while incorporating attention mechanisms that dynamically concentrate on pertinent regions within input images, resulting in enhanced representation learning. Leveraging the Random Forest classifier provides notable benefits, including proficient handling of high-dimensional feature representations, reduction of variance and overfitting concerns, and resilience against noisy and incomplete data. Additionally, the model performance is further optimized through hyperparameter optimization, utilizing the Optuna in conjunction with hill climbing, which efficiently explores the hyperparameter space to discover optimal configurations. The proposed LAVRF model demonstrates outstanding accuracy on three datasets, achieving remarkable results of 99.98%, 99.90%, and 100% on the American Sign Language, American Sign Language with Digits, and NUS Hand Posture datasets, respectively.

Formulation and characterization of polymeric nanoparticle of Rivastigmine for effective management of Alzheimer's disease.

Memory loss or dementia is a progressive disorder, and one of its common forms is Alzheimer's disease (AD), effecting mostly middle aged and older adults. In the present study, we developed Rivastigmine (RIV) nanoparticles using poly(lactic-co-glycolic acid) (RIV-loaded PLGA NPs) and polyvinyl alcohol (PVA). The prepared RIV-PLGA nanoparticles was evaluated for the management of Alzheimer's disease (AD). The nanoparticles were prepared by the slightly modified nano-precipitation technique. The developed formulations were evaluated for particle size, zeta potential (ZP), polydispersibility index (PDI) and surface morphology and drug content. The experimental result revealed that prepared RIV-loaded PLGA NPs (F1) was optimized having particle size (61.2 ± 4.6 nm), PDI (0.292), ZP (-11.2 ± 1.2). SEM study confirms the prepared nanoparticles depicted non-aggregated as well smooth surface particles without any fracture. This formulation (F1) was further assessed for in vivo studies on animal model. A pharmacological screening on an animal model of Alzheimer's disease revealed that RIV-loaded PLGA NPs formulations treat CNS disorders like Alzheimer's effectively. In addition to that, an in-vivo brain cholinesterase estimation study found that, animals treated with optimized formulation significantly (p < 0.01) reduced brain cholinesterase activity when compared to scopolamine-treated animals. According to the above results, it can be concluded that RIV-loaded PLGA NPs are ideal carriers for delivering the drug at a specific target site in the brain, thus may treat Alzheimer's disease efficiently and improve patient compliance.

Investigation of phytochemicals isolated from selected Saudi medicinal plants as natural inhibitors of SARS CoV-2 main protease: In vitro, molecular docking and simulation analysis.

The escalation of many coronavirus variants accompanied by the lack of an effective cure has motivated the hunt for effective antiviral medicines. In this regard, 18 Saudi Arabian medicinal plants were evaluated for SARS CoV-2 main protease (Mpro) inhibition activity. Among them, Terminalia brownii and Acacia asak alcoholic extracts exhibited significant Mpro inhibition, with inhibition rates of 95.3 % and 95.2 %, respectively, at a concentration of 100 µg/mL. Bioassay-guided phytochemical study for the most active n-butanol fraction of T. brownii led to identification of eleven compounds, including two phenolic acids (1, and 2), seven hydrolysable tannins (3-10), and one flavonoid (11) as well as four flavonoids from A. asak (12-15). The structures of the isolated compounds were established using various spectroscopic techniques and comparison with known compounds. To investigate the chemical interactions between the identified compounds and the target Mpro protein, molecular docking was performed using AutoDock 4.2. The findings identified compounds 4, 5, 10, and 14 as the most potential inhibitors of Mpro with binding energies of -9.3, -8.5, -8.1, and -7.8 kcal mol-1, respectively. In order to assess the stability of the protein-ligand complexes, molecular dynamics simulations were conducted for a duration of 100 ns, and various parameters such as RMSD, RMSF, Rg, and SASA were evaluated. All selected compounds 4, 5, 10, and 14 showed considerable Mpro inhibiting activity in vitro, with compound 4 being the most powerful with an IC50 value of 1.2 µg/mL. MM-GBSA free energy calculations also revealed compound 4 as the most powerful Mpro inhibitor. None of the compounds (4, 5, 10, and 14) display any significant cytotoxic activity against A549 and HUVEC cell lines.

Pulmonary Manifestations of Systemic Lupus Erythematosus Among Adults in Aseer Region, Saudi Arabia.

Background: Nearly half of the Systemic lupus erythematosus (SLE) patients develop lung involvement. The study assessed the extent of pulmonary involvement among SLE patients and to identify the associated factors in the population. Methodology: This retrospective cohort study was conducted at Aseer Hospital and Khamis Myshat Hospital in the Southern region of Saudi Arabia. The study spanned from January 1, 2016, to June 3, 2023. Patient inclusion criteria encompassed individuals who received a definitive diagnosis and classification as per American College of Rheumatology criteria, while patients under 18 years of age and those with mixed connective tissue diseases were exclude. Results: A total of 247 participants were included. 41.7% (n=103) aged 41 years and older, 95.1% (n = 235) were females. Around 10.10% had diabetes mellitus and 17.00% had hypertension and hypothyroidism. Lupus Nephritis was in 15.40%. Chest involvement was reported in 21.9%, in the form of pleuritis (6.10%), pleural effusion (4.00%), and lupus pneumonitis (4.00%), interstitial lung disease (4.00%), pulmonary embolism (3.60%) of individuals, and pulmonary hemorrhage (2.80%). The respiratory symptoms reported by SLE were; dyspnea, cough, and chest pain each having a prevalence of around 18.0%. Palpitations have a relatively high occurrence at 13.80%. Meanwhile, hemoptysis (blood coughing) has a lower prevalence of 1.20%, and fever is reported at 2.80%. Having chronic kidney disease and hypertension were significantly associated with having pulmonary involvement; (χ2=3.308, p=0.027) and (χ2=7.782, Fisher's p=0.002) respectively. The seropositivity for antiphospholipid Abs, anti-CCP, and antids-DNA were significantly associated with pulmonary involvement (χ2=3.239, =p=0.049), (χ2=4.621, Fisher's p=0.023), and (χ2=8.248, p=0.010) respectively. Conclusion: The study found that 21.9% of SLE patients experience chest involvement, with varying degrees of pulmonary symptoms. Factors such as chronic kidney disease, hypertension, antiphospholipid antibodies, Anti-CCP positivity, and seropositivity for Anti-dsDNA were found to be significant associations with lung involvement, contributing to our understanding of SLE.

The Awareness, Prevalence, and Risk Factors of Chronic Kidney Disease Among Diabetes Mellitus and Hypertensive Patients in the Aseer Region, Saudi Arabia.

Background and objective Given its ever-increasing burden, chronic kidney disease (CKD) represents a significant public health threat. CKD is characterized by a gradual alteration in the kidney's normal glomerular filtration rate, which results in the progressive loss of kidney function over a period of time ranging from a few months to years. Diabetes mellitus (DM) and hypertension (HTN) are well-known risk factors for developing CKD and end-stage renal failure. In light of this, this study aimed to assess the awareness, prevalence, and risk factors of CKD in patients with diabetes and those with HTN in the Aseer region, the Kingdom of Saudi Arabia. Methods A correlational cross-sectional study was conducted among a sample of people across Saudi Arabia. The data collection was conducted via an online questionnaire circulated on social media platforms. The study questionnaire included socioeconomic and demographic information and medical history of DM, HTN, and CKD. Also, patients' awareness of and attitude towards CKD were assessed. Results A total of 301 diabetic or hypertensive patients fulfilling the inclusion criteria completed the study questionnaire. Of them, 174 (57.8%) were aged less than 55 years, while 127 (42.2%) were aged more than 55 years; 200 (66.4%) patients were males. A total of 94 (31.2%) study patients were diabetic, 64 (21.3%) were hypertensive, and 143 (47.5%) were both diabetic and hypertensive; 226 (75.1%) study patients had an overall good awareness of CKD while only 75 (24.9%) showed a poor awareness level. Higher awareness was associated with patients' age, education, and having CKD, DM, or HTN (p<0.05). Conclusion Our findings revealed that CKD was not common among study patients, and its prevalence was found to be less than estimated based on many studies in the literature. Also, diabetic and hypertensive patients showed a higher than satisfactory level of awareness of CKD, especially young patients with high levels of education.

Cell surface expression of Ribophorin I, an endoplasmic reticulum protein, over different cell types.

Ribophorin-1 serves as one of the subunits of the oligosaccharyltransferase (OST) complex located in the endoplasmic reticulum (ER). Until now, RPN-1 was considered an ER protein. However, our findings reveal that a minor fraction of RPN-1 escapes from the lumen of the ER and is ectopically expressed on the surface of different cell lines. The precise mechanism of protein translocation is unknown. The expression of RPN-1 was demonstrated through the isolation of membrane proteins using surface biotinylation and sucrose density gradient techniques. The confirmation of RPN-1 was obtained through surface staining using a specific antibody, revealing its expression on various cell lines. Additionally, we examined the expression of RPN-1 in different populations of PBMCs and observed a differential regulation of RPN-1 within PBMC subpopulations. Notably, there was a significant expression of RPN-1 on monocytes and B cells, but there was little to no population of T cells expressing RPN-1. We confirmed the expression of RPN-1 on THP-1, U937, and Jurkat cells. We also confirmed their surface expression through si-RNA knockdown. Our study shows RPN-1 expression on various cell surfaces, suggesting varied regulation among cell types. In the future, we may uncover its roles in immune function, signaling, and differentiation/proliferation.

A High-Performance, Low-Cost, and Integrated Hairpin Topology RF Switched Filter Bank for Radar Applications.

Switched filter banks find widespread application in frequency-hopping radar systems and communication networks with multiple operating frequencies, especially in situations demanding elevated filter element isolation. In this paper, the design and implementation of a highly isolated switchable narrow-bandpass filter bank architecture using hairpin microstrip topology is presented. The filter bank has four discrete bandpass filters with passbands of 2.0-2.2 GHz, 2.3-2.5 GHz, 3.1-3.3 GHz, and 3.9-4.1 GHz. These filters span the radar S-frequency band (2.0-4.0 GHz). In order to switch between channels with a switching speed of nanoseconds, low-loss and highly isolated SP4T switches are implemented. Advanced design system (ADS) software is used to design the various filter functionalities, and the entire system is tested on a vector network analyzer (VNA). The proposed architecture makes it much easier to put the filter bank into practice and switch it to the desired frequency, which is useful for radar receiver applications.

Isolation of a novel isoprenylated phenolic compound and neuroprotective evaluation of Dodonaea viscosa extract against cerebral ischaemia-reperfusion injury in rats.

Dodonaea viscosa grows widely in Saudi Arabia, but studies evaluating its neuroprotective activity are lacking. Thus, this study aimed to isolate and identify the secondary metabolites and evaluate the neuroprotective effects of D. viscosa leaves. The isolation and identification of phytochemicals were performed using chromatographic and spectroscopic techniques. The neuroprotective potential of the extract was evaluated against focal cerebral ischaemia-reperfusion injury in rat model. Neurobehavioural deficits in the rats were evaluated, and their brains were harvested to measure infarct volume and oxidative biomarkers. Results revealed the presence of three compounds: a novel isoprenylated phenolic derivative that was elucidated as 4-hydroxy-3-(3'-methyl-2'-butenyl) phenyl 1-O-ß-D-apiosyl-(1''' â†’ 6'')- ß-D-glucopyranoside (named Viscomarfadol) and two known compounds (isorhamnetin-3-O-rutinoside and epicatechin (4-8) catechin). Pre-treatment of the rats with the extract improved neurological outcomes. It significantly reduced neurological deficits and infarct volume; significantly reduced lipid peroxidation, as evidenced by decreased malondialdehyde levels; and significantly elevated antioxidant (superoxide dismutase, catalase, and glutathione) activities. These results indicate that D. viscosa is a promising source of bioactive compounds that can improve neurological status, decrease infarct volume, and enhance antioxidant activities in rats with cerebral ischaemic injury. Thus, D. viscosa could be developed into an adjuvant therapy for ischaemic stroke and other oxidative stress-related neurodegenerative disorders. Further investigations are warranted to explore other bioactive compounds in D. viscosa and evaluate their potential neuroprotective activities.

Assessment of Primary Healthcare Providers' Knowledge and Practices in Addressing Asymptomatic Hyperuricemia and Gout in the Asir Region of Saudi Arabia.

Introduction and aim Gout, the most common form of inflammatory arthritis, arises from hyperuricemia, a condition where elevated levels of uric acid lead to the deposition of monosodium urate (MSU) crystals in the joints. Nevertheless, it's important to note that not all cases of hyperuricemia result in gout. Methodology This cross-sectional study was conducted in the Asir region of Saudi Arabia, targeting primary healthcare physicians (PHPs) specializing in family medicine and general practice. The study utilized a modified electronic questionnaire, inspired by similar studies and aligned with recent guidelines, to assess PHPs' knowledge and practices concerning asymptomatic hyperuricemia (AH) and gout. The questionnaire encompassed the PHPs' demographic data and their knowledge and practices for AH and gout management. Results Out of 201 participating PHPs, the majority were male (68.2%), predominantly aged 25-34 years (73.1%), and practicing as general practitioners (61.2%). A significant proportion of PHPs had less than five years of experience (63.7%). In terms of education, 36.8% attended continuing medical education (CME) on AH or gout, and 66.7% were aware of the related management guidelines. The study revealed that the total knowledge score among PHPs averaged 5.18 out of seven, indicating a moderate level of knowledge. However, their practice level was moderate, with a mean practice score of 6.75 out of 12. The study also found no significant differences in knowledge scores based on gender, age, or years of experience, but significant variations were noted based on medical specialty. Conclusion There is a moderate level of knowledge and practice among PHPs in managing AH and gout in the Asir region. Despite adequate knowledge levels, there appears to be a gap in implementing this knowledge into practice, particularly in long-term management strategies. The findings emphasize the need for ongoing medical education and specialized training programs to bridge these gaps. The study provides a valuable framework for identifying and addressing similar challenges in other regions and medical practices.

Bioprospecting of Meliaceae family phytomolecules for the treatment of monkeypox virus infection: a QSAR modeling and MD simulation approach.

Recent monkeypox virus (MPXV) infections show the risk of MPXV transmission that persists today and the significance of surveillance and quick response methods to stop the virus's spread. Currently, the monkeypox virus infection is not specifically treated. In this study, QSAR models were designed using known inhibitors of cysteine proteinase from the vaccinia virus, where the Random Forest model and Ridge model had showed the best correlation between predicted and observed EC50. These models were used to screen Maliaceae family phytochemicals against MPXV cysteine proteinase. The compound, IMPHY010637 was detected in top 5 from both the QSAR screening models and showed best docked score (-8.6 kcal/mol) and thus selected for further investigation. Further, the IMPHY010637 showed interaction with the catalytic residue His241 of the protein as reported in earlier studies. The ADMET analysis of the compound showed the acceptable drug-like properties of IMPHY010637. However, these properties could be improved after experimental validation of protein-ligand binding. Both docked complex and poses created in 100 ns MD simulation of the protein-ligand complex showed the presence of multiple hydrogen bonds. RMSD and conformation analysis showed stable binding of IMPHY010637 with the cysteine proteinase of MPXV at its active site. Compared to the known inhibitor, IMPHY010637 showed better binding with the protein as observed by the PCA and MM/GBSA analysis. This study concluded IMPHY010637 as a potential inhibitor for the cysteine proteinase of MPXV using computational methods that could be tested in in-vitro experiments.Communicated by Ramaswamy H. Sarma.

Application of temperature-dependent and steered molecular dynamics simulation to screen anti-dengue compounds against Marburg virus.

Marburg virus infections are extremely fatal with a fatality range of 23% to 90%, therefore there is an urgent requirement to design and develop efficient therapeutic molecules. Here, a comprehensive temperature-dependent molecular dynamics (MD) simulation method was implemented to identify the potential molecule from the anti-dengue compound library that can inhibit the function of the VP24 protein of Marburg. Virtual high throughput screening identified five effective binders of VP24 after screening 484 anti-dengue compounds. These compounds were treated in MD simulation at four different temperatures: 300, 340, 380, and 420 K. Higher temperatures showed dissociation of hit compounds from the protein. Further, triplicates of 100 ns MD simulation were conducted which showed that compounds ID = 118717693, and ID = 5361 showed strong stability with the protein molecule. These compounds were further validated using ΔG binding free energies and they showed: -30.38 kcal/mol, and -67.83 kcal/mol binding free energies, respectively. Later, these two compounds were used in steered MD simulation to detect its dissociation. Compound ID = 5361 showed the maximum pulling force of 199.02 kcal/mol/nm to dissociate the protein-ligand complex while ID = 118717693 had a pulling force of 101.11 kcal/mol/nm, respectively. This ligand highest number of hydrogen bonds with varying occupancies at 89.93%, 69.80%, 57.93%, 52.33%, and 50.63%. This study showed that ID = 5361 can bind with the VP24 strongly and has the potential to inhibit its function which can be validated in the in-vitro experiment.Communicated by Ramaswamy H. Sarma.