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Autism spectrum disorder (ASD) is a developmental disorder resulting from variations in brain structure and function. Individuals with ASD often experience challenges in communication and social interaction, along with engaging in repetitive or restricted patterns of behavior and interests. The lack of documented data and the wide range of pathophysiological processes associated with ASD make it challenging to work which causes a major financial burden on health care management. Withania somnifera, often referred to as ashwagandha is the tropical winter cherry in the Solanaceae family that can be used to treat several ailments such as asthma, stress, hypertension, diabetes, cancer, arthritic, and neural disorders including ASD. In this investigation, we examined the active compound-target-pathway network and discovered that Withanolide J, Withanone, and Withaferin A have a great role in the onset of ASD by influencing the IL6 gene. Later, the molecular docking method was applied for confirmation of the active compound's effective action against the prospective target. The molecular dynamics simulation exhibited that the complexes of Withanolide J and Withanone had stable intermolecular binding conformation and unveiled very stable dynamics during the simulation time. A combined network pharmacology and molecular docking approach demonstrated that W. somnifera exhibits a promising preventive impact on ASD by targeting relevant signaling pathways associated with the disorder. This establishes a foundation for comprehending the underlying mechanism of the anti-ASD activity of W. somnifera.

Introduction: The most common primary brain tumor in adults is glioblastoma multiforme (GBM), accounting for 45.2% of all cases. The characteristics of GBM, a highly aggressive brain tumor, include rapid cell division and a propensity for necrosis. Regretfully, the prognosis is extremely poor, with only 5.5% of patients surviving after diagnosis.Methodology: To eradicate these kinds of complicated diseases, significant focus is placed on developing more effective drugs and pinpointing precise pharmacological targets. Finding appropriate biomarkers for drug discovery entails considering a variety of factors, including illness states, gene expression levels, and interactions between proteins. Using statistical techniques like p-values and false discovery rates, we identified differentially expressed genes (DEGs) as the first step in our research for identifying promising biomarkers in GBM. Of the 132 genes, 13 showed upregulation, and only 29 showed unique downregulation. No statistically significant changes in the expression of the remaining genes were observed.Results: Matrix metallopeptidase 9 (MMP9) had the greatest degree in the hub biomarker gene identification, followed by (periostin (POSTN) at 11 and Hes family BHLH transcription factor 5 (HES5) at 9. The significance of the identification of each hub biomarker gene in the initiation and advancement of glioblastoma multiforme was brought to light by the survival analysis. Many of these genes participate in signaling networks and function in extracellular areas, as demonstrated by the enrichment analysis.We also identified the transcription factors and kinases that control proteins in the proteinprotein interactions (PPIs) of the DEGs.Discussion: We discovered drugs connected to every hub biomarker. It is an appealing therapeutic target for inhibiting MMP9 involved in GBM. Molecular docking investigations indicated that the chosen complexes (carmustine, lomustine, marimastat, and temozolomide) had high binding affinities of −6.3, −7.4, −7.7, and −8.7 kcal/mol, respectively, the mean root-mean-square deviation (RMSD) value for the carmustine complex and marimastat complex was 4.2 Å and 4.9 Å, respectively, and the lomustine and temozolomide complex system showed an average RMSD of 1.2 Å and 1.6 Å, respectively. Additionally, high stability in root-mean-square fluctuation (RMSF) analysis was observed with no structural conformational changes among the atomic molecules. Thus, these in silico investigations develop a new way for experimentalists to target lethal diseases in future.

Nano compounds, especially metal-organic frameworks (MOFs), have significant properties. Among the most important properties of these compounds, which depend on their specific surface area and porosity, are biological properties, such as anticancer and antibacterial properties. In this study, a new titanium/BTB metal-organic framework (Ti/BTB-MOF) was synthesized by using titanium nitrate and 1,3,5-Tris(4-carboxyphenyl)benzene (BTB) under microwave radiation. The structure of the synthesized Ti/BTB-MOF was characterized and confirmed using X-ray diffraction (XRD) patterns, X-ray photoelectron spectroscopy (XPS) analysis, Fourier transform infrared (FT-IR) spectra, energy-dispersive X-ray (EDAX) analysis mapping, scanning electron microscope (SEM) images, thermogravimetric analysis (TGA) curves, and Brunauer–Emmett–Teller (BET) analysis. The in vitro anticancer properties of Ti/BTB-MOF were evaluated using the MTT method against MG-63/bone cancer cells and A-431/skin cancer cells. The in vitro antibacterial activity was tested using the Clinical and Laboratory Standards Institute (CLSI) guidelines. In the anticancer activity, IC50 (half-maximal inhibitory concentration) values of 152 μg/mL and 201 μg/mL for MG-63/bone cancer cells and A-431/skin cancer cells, respectively, were observed. In the antibacterial activity, minimum inhibitory concentrations (MICs) of 2–64 μg/mL were observed against studied pathogenic strains. The antimicrobial activity of Ti/BTB-MOF was higher than that of penicillin and gentamicin. Therefore, the synthesized Ti/BTB-MOF could be introduced as a suitable bioactive candidate.

Triple-negative breast cancer (TNBC) comprises 10 % to 20 % of breast cancer, however, it is more dangerous than other types of breast cancer, because it lacks druggable targets, such as the estrogen receptors (ER) and the progesterone receptor (PR), and has under expressed receptor tyrosine kinase, ErbB2. Present targeted therapies are not very effective and other choices include invasive procedures like surgery or less invasive ones like radiotherapy and chemotherapy. This study investigated the potential anticancer activity of some novel quinazolinone derivatives that were designed on the structural framework of two approved anticancer drugs, Ispinesib (KSP inhibitor) and Idelalisib (PI3Kδ inhibitor), to find out solutions for TNBC. All the designed derivatives (3a-l) were subjected to extra precision molecular docking and were synthesized and spectrally characterized. In vitro enzyme inhibition assay of compounds (3a, 3b, 3e, 3 g and 3 h) revealed their nanomolar inhibitory potential against the anticancer targets, KSP and PI3Kδ. Using MTT assay, the cytotoxic potential of compounds 3a, 3b and 3e were found highest against MDA-MB-231 cells with an IC50 of 14.51 µM, 16.27 µM, and 9.97 µM, respectively. Remarkably, these compounds were recorded safe against the oral epithelial normal cells with an IC50 values of 293.60 µM, 261.43 µM, and 222 µM, respectively. The anticancer potential of these compounds against MDA-MB-231 cells was revealed to be associated with their apoptotic activity. This was established by examination with the inverted microscope that revealed the appearance of various apoptotic features like cell shrinkage, apoptotic bodies, and membrane blebbing. Using flow cytometry, the Annexin V/PI-stained cancer cells showed an increase in early and late apoptotic cells. In addition, DNA fragmentation was revealed to occur after treatment with the tested compounds by gel electrophoresis. The relative gene expression of pro-apoptotic and anti-apoptotic genes revealed an overexpression of the P53 and BAX genes and a downregulation of the BCL-2 gene by real-time PCR. So, this work proved that compounds 3a, 3b, and 3e could be developed as anticancer candidates, via their P53-dependent apoptotic activity.

Electrocardiogram (ECG) analysis is widely used by cardiologists and medical practitioners for monitoring cardiac health. A high-performance automatic ECG classification system is challenging because there is difficulty in detecting and categorizing different waveforms in the signal, especially in manual analysis of ECG signals, which means, a better classification system is needed in terms of performance and accuracy. Hence, in this paper, the authors propose an accurate ECG classification and monitoring system called convolutional neural network-k nearest neighbor (CNN-kNN). The proposed method utilizes 1D-CNN and kNN. Unlike the existing techniques, the examined technique does not need training during classifying the ECG signals. The CNN-kNN is evaluated against the PhysioNet’s MIT-BIH and PTB diagnostics datasets. The CNN is fed using the ECG beat raw signal directly. In addition, the learned features are extracted from the 1D-CNN model and its dimensions are reduced using two fully connected layers and then fed to the k-NN classifier. The CNN-kNN model achieved average accuracies of 98% and 97.4% on arrhythmia and myocardial infarction classifications, respectively. These results are evidence of the great ability of the proposed model compared to the mentioned models in this article.

Abstract The acquired drug resistance by Mycobacterium tuberculosis (M. tuberculosis) to antibiotics urges the need for developing novel anti-M. tuberculosis drugs that possess novel mechanism of action. Since traditional drug discovery is a labor-intensive and costly process, computer aided drug design is highly appreciated tool as it speeds up and lower the cost of drug development process. Herein, Asinex antibacterial compounds were virtually screened against thioesterase domain of Polyketide synthase 13, a unique enzyme that forms α-alkyl β-ketoesters as a direct precursor of mycolic acids which are essential components of the lipid-rich cell wall of M. tuberculosis. The study identified three drug-like compounds as the most promising leads; BBB_26582140, BBD_30878599 and BBC_29956160 with binding energy value of − 11.25 kcal/mol, − 9.87 kcal/mol and − 9.33 kcal/mol, respectively. The control molecule binding energy score is -9.25 kcal/mol. Also, the docked complexes were dynamically stable with maximum root mean square deviation (RMSD) value of 3 Å. Similarly, the MM-GB\PBSA method revealed highly stable complexes with mean energy values

In this study, we report the synthesis of a new type of chiral crystalline organic porous salt CF2 derived from the ionic reaction between tetrakis(4‐sulfophenyl)methane (TSPM) and the tetra‐(S)‐prolylamide of tetrakis(4‐aminophenyl)methane, (S)‐TPPM, and its ability to stabilize 2 nm palladium nanoparticles to give a novel, nonpyrophoric, chiral, catalytic material Pd@CF2. The preparation of the catalyst was very simple and conducted in water. The heterogeneous catalytic performance of Pd@CF2 was tested in hydrogen reductions of olefins and substituted nitroaromatic compounds using Pd/C as a comparison to determine the specific features of the novel catalyst. Although both types of catalysts exhibited similar catalytic activity in case of reductions of diphenylacetylene and nitrobenzene, Pd@CF2 predominantly promoted the reduction of p‐nitrobenzaldehyde to p‐aminobenzyl alcohol whereas Pd/C gave p‐toluidine. The reduction of p‐dinitrobenzene led to predominant formation of p‐nitrophenylhydroxylamine if promoted by the novel catalyst and to a mixture of products if promoted by Pd/C. In addition, the introduction of p‐alkoxy groups onto nitrobenzenes slowed down the reduction with Pd@CF2 but had no influence on Pd/C activity. A hypothesis ascribing these observations to dissimilar equilibrium distributions of nitro and polar groups within the organic framework and the palladium metal surface is proposed to rationalize the selectivity of the novel catalytic material. [ABSTRACT FROM AUTHOR]

This paper introduces the modified Kies Topp-Leone (MKTL) distribution for modeling data on the (0, 1) or [0, 1] interval. The shapes of the density and hazard rate functions manifest desirable shapes, making the MKTL distribution suitable for modeling data with different characteristics at the unit interval. Twelve different estimation methods are utilized to estimate the distribution parameters, and Monte Carlo simulation experiments are executed to assess the performance of the methods. The simulation results suggest that the maximum likelihood method is the superior method. The usefulness of the new distribution is illustrated by utilizing three data sets, and its performance is juxtaposed with that of other competing models. The findings affirm the superiority of the MKTL distribution over the other candidate models. Applying the developed quantile regression model using the new distribution disclosed that it offers a competitive fit over other existing regression models. [ABSTRACT FROM AUTHOR]

The importance of convex and non-convex functions in the study of optimization is widely established. The concept of convexity also plays a key part in the subject of inequalities due to the behavior of its definition. The principles of convexity and symmetry are inextricably linked. Because of the considerable association that has emerged between the two in recent years, we may apply what we learn from one to the other. In this study, first, Hermite-Hadamard type inequalities for LR-p-convex interval-valued functions (LR-p-convex-I-V-F) are constructed in this study. Second, for the product of p-convex various Hermite-Hadamard (HH) type integral inequalities are established. Similarly, we also obtain Hermite-Hadamard-Fejér (HH-Fejér) type integral inequality for LR-p-convex-I-V-F. Finally, for LR-p-convex-I-V-F, various discrete Schur's and Jensen's type inequalities are presented. Moreover, the results presented in this study are verified by useful nontrivial examples. Some of the results reported here for be LR-p-convex-I-V-F are generalizations of prior results for convex and harmonically convex functions, as well as p-convex functions.

Present research work evaluates variation in volatile chemicals profile and biological activities of essential oil (EO) obtained from the leaves of eucalyptus ( Eucalyptus camaldulensis Dehnh.) using hydro-distillation (HD) and supercritical fluid extraction (SFE). The yield (1.32%) of volatile oil by HD was higher than the yield (.52%) of the SFE method ( P < .05). The results of physical factors like density, color, refractive index, and solubility of the EOs produced by both the methods showed insignificant variations. Gas chromatography - mass spectrometry (GC-MS) compositional analysis showed that eucalyptol (31.10% and 30.43%) and α -pinene (11.02% and 10.35%) were the main constituents detected in SFE and HD extracted Eucalyptus camaldulensis EO, respectively. Antioxidant activity-related parameters, such as reducing ability and DPPH free radical scavenging capability exhibited by EO obtained via SFE were noted to be better than hydro-distilled EO. Supercritical fluid extracted and hydro-distilled essential oils demonstrated a considerable but variable antimicrobial potential against selected bacterial and fungal strains. Interestingly, oil extracted by SFE showed relatively higher hemolytic activity and biofilm inhibition potential. The variation in biological activities of tested EOs can be linked to the difference in the volatile bioactives composition due to different isolation techniques. In conclusion, the EO obtained from Eucalyptus leaves by the SFE method can be explored as a potential antioxidant and antimicrobial agent in the functional food and nutra-pharmaceutical sector.

The discovery of multi-targeted kinase inhibitors emerged as a potential strategy in the therapy of multi-genic diseases, such as cancer, that cannot be effectively treated by modulating a single biological function or pathway. The current work presents an extension of our effort to design and synthesize a series of new quinazolin-4-one derivatives based on their established anti-cancer activities as inhibitors of multiple protein kinases. The cytotoxicity of the new derivatives was evaluated against a normal human cell line (WI-38) and four cancer lines, including HepG2, MCF-7, MDA-231, and HeLa. The most active compound, 5d, showed broad-spectrum anti-cancer activities against all tested cell lines (IC50 = 1.94–7.1 µM) in comparison to doxorubicin (IC50 = 3.18–5.57 µM). Interestingly, compound 5d exhibited lower toxicity in the normal WI-38 cells (IC50 = 40.85 µM) than doxorubicin (IC50 = 6.72 µM), indicating a good safety profile. Additionally, the potential of compound 5d as a multi-targeted kinase inhibitor was examined against different protein kinases, including VEGFR2, EGFR, HER2, and CDK2. In comparison to the corresponding positive controls, compound 5d exhibited comparable activities in nanomolar ranges against HER2, EGFR, and VEGFR2. However, compound 5d was the least active against CDK2 (2.097 ± 0.126 µM) when compared to the positive control roscovitine (0.32 ± 0.019 µM). The apoptotic activity investigation in HepG2 cells demonstrated that compound 5d arrested the cell cycle at the S phase and induced early and late apoptosis. Furthermore, the results demonstrated that the apoptosis pathway was provoked due to an upregulation in the expression of the proapoptotic genes caspase-3, caspase-9, and Bax and the downregulation of the Bcl-2 anti-apoptotic gene. For the in silico docking studies, compound 5d showed relative binding interactions, including hydrogen, hydrophobic, and halogen bindings, with protein kinases that are similar to the reference inhibitors.

Mohammad Abdulwahab,1 Mohammad Kamal,1 Ahmad M AlAli,1 Yousif M Husain,2 Maryam Safar3 1Department of Surgical Sciences, Faculty of Dentistry, Health Sciences Center, Kuwait University, Kuwait City, Kuwait; 2Department of Family Medicine, Ministry of Health, Kuwait City, Kuwait; 3Faculty of Dentistry, Health Sciences Center, Kuwait University, Kuwait City, KuwaitCorrespondence: Mohammad AbdulwahabDepartment of Surgical Sciences, Faculty of Dentistry, Health Sciences Center, Kuwait University, Safat, Kuwait City, 12027, KuwaitEmail mohammad.abdulwahab@ku.edu.kwPurpose: The aim of this study was to assess the levels of awareness and knowledge regarding COVID-19 among healthcare professionals and students in Kuwait and the Gulf Cooperation Council (GCC) region.Materials and Methods: An online cross-sectional survey using a previously validated questionnaire was used to assess the awareness and knowledge levels of COVID-19 in this study, which was distributed via various online platforms to include as many participants as possible. The study targeted medical doctors, dentists, medical students, and dental students from the GCC region. Participants were at least 18 years old, voluntarily consented to complete the questionnaire, and were assured that their responses would remain anonymous. Permission was obtained using a previously validated and applied questionnaire to assess their level of awareness and knowledge regarding COVID-19. To compare the proportional responses between groups in descriptive statistics, a proportion Z-test was used to find any significant differences, with a statistical significance set at p < 0.05.Results: A total of 1621 participants responded to our questionnaire. Almost two-thirds of the respondents were female (64.4%), and the majority were less than 25 years old (67.2%). Dentists accounted for 12.6% of the total population, and their clinical experience ranged from 0.25 to 52 years (median: 3 years, IQR: 1– 10 years). Meanwhile, medical doctors accounted for 24.5% of the responses, and their clinical experience ranged from 0 to 50 years (median: 13 years, IQR: 3– 23 years). Among the dental students, 42.3% were in the pre-clinical years, while 57.7% were in the clinical years of study. However, among medical students, 57.5% were in the pre-clinical years, while 42.5% were in the clinical years of study.Conclusion: Our results demonstrated that dental students, medical doctors, and medical students exhibited higher odds of having satisfactory COVID-19 perception scores than dentists.Keywords: coronavirus, SARS-CoV-2, pandemic, COVID-19, knowledge, perception, healthcare, survey, healthcare worker, medical students

Tyrosine kinase inhibitors have often been reported to treat early-stage hormone-receptor-positive breast cancers. In particular, neratinib has shown positive responses in stage I and II cases in women with HER2-positive breast cancers with trastuzumab. In order to augment the biopharmaceutical attributes of the drug, the work designed endeavors to explore the therapeutic benefits of neratinib in combination with naringenin, a phytoconstituent with reported uses in breast cancer. A UPLC-MS/MS method was developed for the simultaneous estimation of neratinib and naringenin in rat plasma, while imatinib was selected as the internal standard (IS). Acetonitrile was used as the liquid extractant. The reversed-phase separation was achieved on a C18 column (100 mm × 2.1 mm, 1.7 µm) with the isocratic flow of mobile phase-containing acetonitrile (0.1% formic acid) and 0.002 M ammonium acetate (50:50, % v/v) at flow rate 0.5 mL·min−1. The mass spectra were recorded by multiple reaction monitoring of the precursor-to-product ion transitions for neratinib (m/z 557.138→111.927), naringenin (m/z 273.115→152.954), and the IS (m/z 494.24→394.11). The method was validated for selectivity, trueness, precision, matrix effect, recovery, and stability over a concentration range of 10–1280 ng·mL−1 for both targets and was acceptable. The method was also assessed for greenness profile by an integrative qualitative and quantitative approach; the results corroborated the eco-friendly nature of the method. Therefore, the developed method has implications for its applicability in clinical sample analysis from pharmacokinetic studies in human studies to support the therapeutic drug monitoring (TDM) of combination drugs.

The unit exponentiated half logistic power series (UEHLPS), a family of compound distributions with bounded support, is introduced in this study. This family is produced by compounding the unit exponentiated half logistic and power series distributions. In the UEHLPS class, some interesting compound distributions can be found. We find formulas for the moments, density and distribution functions, limiting behavior, and other UEHLPS properties. Five well-known estimating approaches are used to estimate the parameters of one sub-model, and a simulation study is created. The simulated results show that the maximum product of spacing estimates had lower accuracy measure values than the other estimates. Ultimately, three real data sets from various scientific areas are used to analyze the performance of the new class.

Background and Objectives: The identification of driver genes, which initiate cancer in cells, holds immense significance within the field of oncology. Discovering these genes is crucial for identifying markers that can indicate specific conditions or diseases, as well as for developing novel systemic and molecular treatment approaches for them. Several computational methods have been developed to identify the genes responsible for cancer based on genomic data. However, many of these methods find key mutations in genomic data to predict which genes are responsible for cancer. These methods rely on mutation and genomic data, but they often exhibit a high rate of false positives in the results. In this study, we propose an influence maximization-based approach, called CinfuMax, which can predict cancer-associated genes without relying on mutation information. Methods: In this method, the concept of influence maximization and the independent cascade model is employed. Firstly, the gene regulatory network for breast, lung and colon cancers was constructed using regulatory interactions and gene expression data. Next, we implemented an independent cascade diffusion algorithm on the networks to calculate the coverage of each gene. Ultimately, genes with the highest coverage were identified and classified as drivers. Results: The proposed method's results were compared to those of 19 other computational and network-based methods, utilizing the F-measure and the number of predicted driver genes as evaluation metricsThe results clearly indicate that the proposed method outperforms other methods. Furthermore, CinfuMax successfully identifies 18, 19, and 22 individual driver genes in breast, lung, and colon cancers, respectively, which were not previously identified by any other methods. Conclusion: Corrected: The results indicate that independent cascading methods for identifying driver genes outperform linear threshold methods. Driver genes were also categorized based on their influence speed, and the genes with the highest diffusion rate in each type of cancer have been identified. The identification of these genes can be valuable for molecular therapies and drug development. [ABSTRACT FROM AUTHOR]

The papain-like protease (PLpro) is vital for the replication of coronaviruses (CoVs), as well as for escaping innate-immune responses of the host. Hence, it has emerged as an attractive antiviral drug-target. In this study, computational approaches were employed, mainly the structure-based virtual screening coupled with all-atom molecular dynamics (MD) simulations to computationally identify specific inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PLpro, which can be further developed as potential pan-PLpro based broad-spectrum antiviral drugs. The sequence, structure, and functional conserveness of most deadly human CoVs PLpro were explored, and it was revealed that functionally important catalytic triad residues are well conserved among SARS-CoV, SARS-CoV-2, and middle east respiratory syndrome coronavirus (MERS-CoV). The subsequent screening of a focused protease inhibitors database composed of ∼7,000 compounds resulted in the identification of three candidate compounds, ADM_13083841, LMG_15521745, and SYN_15517940. These three compounds established conserved interactions which were further explored through MD simulations, free energy calculations, and residual energy contribution estimated by MM-PB(GB)SA method. All these compounds showed stable conformation and interacted well with the active residues of SARS-CoV-2 PLpro, and showed consistent interaction profile with SARS-CoV PLpro and MERS-CoV PLpro as well. Conclusively, the reported SARS-CoV-2 PLpro specific compounds could serve as seeds for developing potent pan-PLpro based broad-spectrum antiviral drugs against deadly human coronaviruses. Moreover, the presented information related to binding site residual energy contribution could lead to further optimization of these compounds.

The aim of this work is to evaluate the chemical constituents and potential biological activists of Cunninghamella blakesleeana. Three fatty acids were isolated using column chromatography and identified as palmitic acid (F1), oleic acid (F2) and stearic acid (F3) in addition to other two steroidal compounds; α-amyrin (A4), and β-sitosterol (A5). Using GC, ten fatty acids were detected the major fatty acid obtained was stearic acid (74.61%) while palmitic acid was the second high percentage (10.35%), and the least percentage obtained was arachidic acid (0.07%). C. blakesleeana extract showed in-vitro antimicrobial activities against some microorganisms. The highest activity of C. blakesleeana total extract was reported against Staphylococcus aureus (18.3 ± 0.03 mm.) followed by Streptococcus pyogenes (15.3 ± 0.05), while the lowest were for both Candida albicans & Pseudomonas aeruginosa (6.7 ± 0.06 and 5.9.0 ± 0.9 mm. respectively). The three isolated compounds (F1-3) showed activities against Staphylococcus aureus, Penicillium expansum, and Salmonella typhimurium only. The highest activity was aganist Staphylococcus aureus (13.0 ± 0.1 mm.). The highest effect was obtained by compound F3 (stearic acid) (15.0 ± 0.5 mm.), and compound F1 (oleic acid) (13.0 ± 0.1 mm.) and F2 (palmitic acid) 11.0 ± 0.3 mm. The total ethanol extract of the investigated fungus was safe up to 5000 mg kg−1 and did not produce any significant change in liver and kidney functions after oral administration (400 mg kg−1) for 14 consecutive days. The results reported the isolation of some fungal new driving compounds which has been not isolated before from Cunninghamella species in addition to their correlated new biological activities.

Coronavirus disease 2019 (COVID-19) has affected almost every country in the world by causing a global pandemic with a high mortality rate. Lack of an effective vaccine and/or antiviral drugs against SARS-CoV-2, the causative agent, has severely hampered the response to this novel coronavirus. Natural products have long been used in traditional medicines to treat various diseases, and purified phytochemicals from medicinal plants provide a valuable scaffold for the discovery of new drug leads. In the present study, we performed a computational screening of an in-house database composed of ~1000 phytochemicals derived from traditional Saudi medicinal plants with recognised antiviral activity. Structure-based virtual screening was carried out against three druggable SARS-CoV-2 targets, viral RNA-dependent RNA polymerase (RdRp), 3-chymotrypsin-like cysteine protease (3CLpro) and papain like protease (PLpro) to identify putative inhibitors that could facilitate the development of potential anti-COVID-19 drug candidates. Computational analyses identified three compounds inhibiting each target, with binding affinity scores ranging from −9.9 to −6.5 kcal/mol. Among these, luteolin 7-rutinoside, chrysophanol 8-(6-galloylglucoside) and kaempferol 7-(6″-galloylglucoside) bound efficiently to RdRp, while chrysophanol 8-(6-galloylglucoside), 3,4,5-tri-O-galloylquinic acid and mulberrofuran G interacted strongly with 3CLpro, and withanolide A, isocodonocarpine and calonysterone bound tightly to PLpro. These potential drug candidates will be subjected to further in vitro and in vivo studies and may assist the development of effective anti-COVID-19 drugs.

The recent pandemic of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has raised global health concerns. The viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme controls coronavirus replication and is essential for its life cycle. 3CLpro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV. Therefore, herein, we analysed the 3CLpro sequence, constructed its 3D homology model, and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds. Our analyses revealed that the top nine hits might serve as potential anti- SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19.

The half-logistic modified Kies exponential (HLMKEx) distribution is a novel three-parameter model that is introduced in the current work to expand the modified Kies exponential distribution and improve its flexibility in modeling real-world data. Due to its versatility, the density function of the HLMKEx distribution offers symmetrical, asymmetrical, unimodal, and reversed-J-shaped, as well as increasing, reversed-J shaped, and upside-down hazard rate forms. An infinite linear representation can be used to represent the HLMKEx density. The HLMKEx model’s fundamental mathematical features are obtained, such as the quantile function, moments, incomplete moments, and moments of residuals. Additionally, some measures of uncertainty as well as stochastic ordering are derived. To estimate its parameters, eight estimation methods are used. With the use of detailed simulation data, we compare the performance of each estimating technique and obtain partial and total ranks for the accuracy measures of absolute bias, mean squared error, and mean absolute relative error. The simulation results demonstrate that, in contrast to other competing distributions, the proposed distribution can actually fit the data more accurately. Two actual data sets are investigated in the field of engineering to demonstrate the adaptability and application of the suggested distribution. The findings demonstrate that, in contrast to other competing distributions, the provided distribution can actually fit the data more accurately.

It is well known that ranked set sampling (RSS) is superior to conventional simple random sampling (SRS) in that it frequently results in more effective inference techniques. One of the most popular and broadly applicable models for lifetime data is the Weibull distribution. This article proposes different modified goodness-of-fit tests based on the empirical distribution function (EDF) for the Weibull distribution. The recommended RSS tests are compared to their SRS counterparts. For each scheme, the critical values of the relevant test statistics are computed. A comparison of the power of the suggested goodness-of-fit tests based on a number of alternatives is investigated. RSS tests are more effective than their SRS equivalents, according to simulated data.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has stressed the global health system to a significant level, which has not only resulted in high morbidity and mortality but also poses a threat for future pandemics. This situation warrants efforts to develop novel therapeutics to manage SARS-CoV-2 in specific and other emerging viruses in general. This study focuses on SARS-CoV2 RNA-dependent RNA polymerase (RdRp) mutations collected from Saudi Arabia and their impact on protein structure and function. The Saudi SARS-CoV-2 RdRp sequences were compared with the reference Wuhan, China RdRp using a variety of computational and biophysics-based approaches. The results revealed that three mutations—A97V, P323I and Y606C—may affect protein stability, and hence the relationship of protein structure to function. The apo wild RdRp is more dynamically stable with compact secondary structure elements compared to the mutants. Further, the wild type showed stable conformational dynamics and interaction network to remdesivir. The net binding energy of wild-type RdRp with remdesivir is -50.76 kcal/mol, which is more stable than the mutants. The findings of the current study might deliver useful information regarding therapeutic development against the mutant RdRp, which may further furnish our understanding of SARS-CoV-2 biology.