Synthesis and bioactivity of a novel surfactin-based lipopeptide for mRNA delivery.

The effective delivery of messenger ribonucleic acid (mRNA) to specific cell types and target tissues poses a significant challenge in nonviral therapeutic strategies. Lipid-based nanoparticles (LNPs) have emerged as a leading carrier system for delivering mRNA, particularly for infectious diseases, such as COVID-19. This study aimed to describe the synthesis of a novel lipopeptide based on surfactin, a naturally occurring surfactant. Additionally, a series of novel LNPs were rationally designed, based on the modified surfactin, OleSurf, and were formulated and optimized. The physicochemical properties, morphologies, and stabilities of the particles were evaluated. All formulations containing OleSurf produced particles with a diameter <80 nm and an encapsulation efficiency >95%. OleSurf LNPs demonstrated excellent transfection efficiency and luciferase expression with no cytotoxicity, compared to lipofectamine 2000, a known transfection reagent, and were comparable to the DLin-MC3-DMA lipid. OleSurf-based LNPs behaved as efficient mRNA carriers and showed enhanced mRNA-binding capabilities, associated with facilitated intracellular release, endosomal escape, and protection from endonuclease degradation. In addition, OleSurf-LNPs showed a higher mRNA delivery efficiency, a more advantageous biodistribution pattern, and an improved safety profile in vivo. Overall, the novel OleSurf LNPs presented an optimal delivery platform for mRNA therapeutics.

Rational design of a multi-epitope vaccine against heartland virus (HRTV) using immune-informatics, molecular docking and dynamics approaches.

Heartland virus (HRTV) is a single-stranded negative-sense RNA virus that infects human beings. Because there are no antiviral medications available to treat HRTV infection, supportive care management is used in cases of severe disease. Therefore, it has spurred research into developing a multi-epitope vaccine capable of providing effective protection against HRTV infection. A multi-epitope vaccine was created using a combination of immuno-informatics, molecular docking and molecular dynamics simulation in this investigation. The HRTV proteome was utilized to predict B-cell, T-cell (HTL and CTL), and IFN-epitopes. Following prediction, highly antigenic, non-allergenic and immunogenic epitopes were chosen, including 6 CTL, 8 HTL, and 5 LBL epitopes that were connected to the final peptide by AAY, GPGPG, and KK linkers, respectively. An adjuvant was introduced to the vaccine's N-terminal through the EAAAK linker to increase its immunogenicity. Following the inclusion of linkers and adjuvant, the final construct has 359 amino acids. The presence of B-cell and IFN-γ-epitopes validates the construct's acquired humoral and cell-mediated immune responses. To ensure the vaccine's safety and immunogenicity profile, its allergenicity, antigenicity, and various physicochemical characteristics were assessed. Docking was used to assess the binding affinity and molecular interaction between the vaccination and TLR-3. In silico cloning was used to confirm the construct's validity and expression efficiency. The results of these computer assays demonstrated that the designed vaccine is highly promising in terms of developing protective immunity against HRTV; nevertheless, additional in vivo and in vitro investigations are required to validate its true immune-protective efficiency.

Rapid UV-Vis spectrophotometric method aided by firefly-PLS models for the simultaneous quantification of ciprofloxacin, lomefloxacin, and enrofloxacin in their laboratory mixture, dosage forms and water samples: greenness and blueness assessment.

Herein, a novel UV spectrophotometric method coupled with chemometric tools was developed for the simultaneous determination of three fluoroquinolone antibiotics: ciprofloxacin, lomefloxacin, and enrofloxacin. Such integration of UV spectroscopy and chemometric analysis proved to be a simple, rapid, and cost-effective approach for the quantification of these clinically important pharmaceutical compounds and aid in their quality control analysis. The method employed firefly algorithm for variable selection and partial least squares (PLS) regression for model calibration. The developed method was validated by independent test set in addition the accuracy, intra and inter-day precision as per ICH guidelines which showed a satisfactory performance with mean recovery ranged between 98.18 and 101.83 with %RSD < 2. Besides, the developed method displayed ultrasensitive levels with LODs (0.0803, 0.1125, 0.1309 µg/mL) and LOQs (0.2434, 0.3409, 0.3968 µg/mL) for ciprofloxacin, lomefloxacin, and enrofloxacin, respectively. The greenness and blueness of the developed method were also evaluated using the recently proposed Analytical GREEnness metric approach (AGREE) and Blue applicability grade index (BAGI) tools, which showed a high AGREE score of 0.79 and a BAGI score of 77.5. These results indicate that the developed method provides an environmentally friendly alternative to the traditionally used chromatographic techniques, while maintaining high analytical practicability. Finally, the application of the developed methodology was demonstrated on real pharmaceutical and tap water samples, and the results were in good agreement with those obtained by the reference HPLC method indicating the reliability and suitability of the proposed spectrophotometric method for routine analysis of fluoroquinolone antibiotics.

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.

Randomized Clinical Trial on Direct Composite and Indirect Ceramic Laminate Veneers in Multiple Diastema Closure Cases: Two-Year Follow-Up.

In recent years, laminate veneer restorations should be considered as a minimally invasive treatment option for several aesthetic reasons. This study compared direct composite veneers' and indirect ceramic laminate veneers' longevity in multiple diastema closures. A total of 28 patients with a mean age of 26 years received 60 direct resin composite (Estelite Asteria; n = 14) and 60 indirect ceramic veneers (IPS e.max Press; n = 14) on the maxillary anterior teeth with diastema closure. Veneers were evaluated at baseline and thereafter every 6 months for up to 2 years using USPHS criteria. Data were analyzed with Fisher's exact and chi-squared tests, while Kaplan-Meier curve was used to assess time to event. In total, three failures were observed in the form of debonding (n = 1) and fracture (n = 2) in the indirect ceramic veneers. No significant difference was observed between the survival rates of composite and ceramic veneers (Estelite Asteria: 93.4%, IPS e.max Press: 95%; p > 0.05). The overall survival rate was 94.2% (Kaplan-Meier). Staining (n = 11) and roughness (n = 14) were frequently observed for the resin composite veneers up to the final recall. Thereby, the preliminary results from this clinical trial comparing two veneer materials indicated that their survival rates were statistically similar. However, surface quality changes were more frequent in the composite veneer material.

Total Hip Arthroplasty Complications in Sickle Cell Disease: Systematic Review and Meta-Analysis.

Background: Microvascular occlusions caused by sickle-shaped erythrocytes in patients with sickle cell disease (SCD) can lead to increased intraoperative and postoperative complications during total hip arthroplasty (THA). This systematic review and meta-analysis aimed to estimate the overall rate of complications following THA in patients with SCD and to identify the predictors of these complications including the surgical approach. Methods: The search was conducted across the grey literature, Google Scholar, and seven databases: Scopus, MEDLINE Central/PubMed, ProQuest, SciELO, SAGE, and Web of Science. All observational studies reporting the proportional THA complications in SCD were included. The Newcastle-Ottawa Scale quality assessment tool was used to assess the quality of the studies. The random effect model was applied to estimate the pooled outcomes. A sub-group analysis for the different approaches was performed. A sensitivity analysis and meta-regression were used to explain heterogeneity and to identify the THA complication predictors. Results: Of 3230 citations, only 23 studies were eligible for the meta-analysis. The pooled proportion of total primary THA complications in patients with SCD was 42% (95% CI: 30-56%, I2 = 95%). The sub-group analysis highlighted the anterolateral approach as the approach accompanied with the least complications. The meta-regression revealed that the anterolateral approach decreases the complications significantly, -28.67 (95%CI, -56.45--0.88, p = 0.044), while the number of hips increased the complications by 0.43 (95%CI, 0.30-0.57, p < 0.001). Male gender, age, lateral approach, and HbSS non-significantly affect the THA complications in SCD 52.05, 0.18, 6.06, and 55.78, respectively. The pooled proportions for an SCD crisis 9% (95%CI, 5-14%, I2 = 61%), dislocation 4% (95%CI: 2-7%, I2 = 66%), aseptic loosening 12% (95%CI, 7-20%, I2 = 91%), revision 6% (3-11, I2 = 92%), heterotopic ossification 12% (95%CI, 3-35%, I2 = 95%), and prosthetic joint infection (PJI) 6% (95%CI, 3-11%, I2 = 92%). The most fitted model of meta-regression illustrated that HbSS significantly increases PJI, 0.05 (95%CI: 0.02-0.08, p = 0.009), and male gender and age non-significantly increase PJI, 2.28 (95%CI: -4.99-13.56, p = 0.311) and 0.001 (95%CI: -0.27-0.27, p = 0.990), respectively. Meanwhile, the anterolateral, lateral, and posterior approaches non-significantly decrease PJI, -3.55, -0.92, and -1.27, respectively. The pooled proportion for a sickle cell disease crisis after revision was 16% (95%CI: 6-36%, I2 = 0) and for aseptic loosening after revision, it was 24% (95%CI: 12-43%, I2 = 0). Conclusions: This study revealed the high rate of complications in patients with SCD and highlighted that the anterolateral approach was associated with the lowest rate of complications. Furthermore, this study illustrated that homozygous (HbSS) individuals are more susceptible to prosthetic joint infection.

Kite Versus Ponseti Method in the Treatment of Idiopathic Congenital Clubfoot: A Systematic Review and Meta-Analysis.

Kite and Ponseti methods are two popular manipulating methods for correcting the deformity of idiopathic congenital clubfoot. We aimed to compare the efficacy of Kite and Ponseti methods in the treatment of children with idiopathic congenital clubfoot. A search was launched on Medline/PubMed, Cochrane Central Register of Controlled Trials, the Web of Science, ProQuest, and Scopus without limits, from inception to May 1, 2024. The outcomes included the rates of initial correction and relapse (primary) as well as the number of casts and duration of treatment (secondary). Mean difference (MD) and risk ratio (RR) were calculated for numerical and dichotomous outcomes, respectively, with 95% confidence intervals (CIs). Nine studies were included. Meta-analysis showed the Ponseti method is significantly associated with a higher probability of correction (n = 6, RR = 1.23 [95% CI = 1.14, 1.32], p < 0.001) and a lower risk of relapse (n = 5, RR = 0.50 [95% CI = 0.36, 0.71], p < 0.001) compared to the Kite method. The Ponseti method utilized a lower number of casts (MD = -3.0 [95% CI = -5.8, -0.2], p = 0.04) and took a shorter duration (MD = -39.76 [95% CI = -67.22, -12.30], p = 0.02) than the Kite method. Evidence suggests that the Ponseti method results in better outcomes than the Kite method in terms of successful initial correction and lower relapse rates. However, the available studies showed varying degrees of risk of bias, and the length of follow-up was inadequate in some studies.

The impact of imposter syndrome on self-esteem and intention to quit among respiratory therapy (RT) students in Saudi Arabia.

Introduction: Imposter syndrome is common among health disciplinary students, leading to serious consequences. However, the impact of imposter syndrome on self-esteem and quitting intention among respiratory therapy students has not been well researched. Objective: To report on the prevalence of imposter syndrome and assess its impacts on self-esteem and quitting intention among respiratory therapy students in Saudi Arabia. Methods: A nonprobability cross-sectional questionnaire using the Clance Impostor Phenomenon Scale and the Rosenberg Self-Esteem Scale was self-administered and distributed among respiratory therapy students between October 2022 and April 2023. Data analysis was performed using Descriptive and inferential statistics. Results: Of the 1500 respiratory therapy students invited to participate in the study, 901 surveys were completed; and thus, included in the final analysis. Of whom, 92% were presented with imposter syndrome: 44% with moderate, 35% with frequent, and 13% with intense feelings. In addition, 60% of respiratory therapy students and interns experienced low self-esteem, while only 0.5% indicated high self-esteem. More than 50% of the study participants thought about quitting the respiratory therapy program, and 30% have been diagnosed with psychological disorders. Furthermore, there was a significant association between imposter syndrome and low self-esteem, p < 0.001. Factors associated with imposter syndrome and low self-esteem were family income (<0.005) and parents' education (<0.005), quitting intention (<0.005), and having been diagnosed with psychological disorders (<0.005). Genders, academic levels, and grade point average were not associated with either imposter syndrome or self-esteem (>0.005). Conclusion: Imposter syndrome and low self-esteem are prevalent among respiratory therapy students, both of which are associated with considering leaving the respiratory therapy program. Effective interventions should be implemented to ameliorate the symptoms imposter syndrome and low self-esteem; thus, improving the academic experience of respiratory therapy students.

Posaconazole-hemp seed oil loaded nanomicelles for invasive fungal disease.

Invasive fungal infections (IFI) pose a significant health burden, leading to high morbidity, mortality, and treatment costs. This study aims to develop and characterize nanomicelles for the codelivery of posaconazole and hemp seed oil for IFI via the oral route. The nanomicelles were prepared using a nanoprecipitation method and optimized through the Box Behnken design. The optimized nanomicelles resulted in satisfactory results for zeta potential, size, PDI, entrapment efficiency, TEM, and stability studies. FTIR and DSC results confirm the compatibility and amorphous state of the prepared nanomicelles. Confocal laser scanning microscopy showed that the optimized nanomicelles penetrated the tissue more deeply (44.9µm) than the suspension (25µm). The drug-loaded nanomicelles exhibited sustained cumulative drug release of 95.48 ± 3.27% for 24 h. The nanomicelles showed significant inhibition against Aspergillus niger and Candida albicans (22.4 ± 0.21 and 32.2 ± 0.46 mm, respectively). The pharmacokinetic study on Wistar rats exhibited a 1.8-fold increase in relative bioavailability for the nanomicelles compared to the suspension. These results confirm their therapeutic efficacy and lay the groundwork for future research and clinical applications, providing a promising synergistic antifungal nanomicelles approach for treating IFIs.

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".

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.

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.

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.

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.

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.

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.

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.

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.

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.