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Molecular activity of bioactive phytocompounds for inhibiting host cell attachment and membrane fusion interacting with West Nile Virus envelope glycoprotein

West Nile virus is an arbovirus primarily spread by mosquitoes, which are the principal carriers and belong to the Flaviviridae category. This widespread disease lacks specific treatments despite its potential lethality, urgently demanding novel pharmaceutical research and development aims to prevent severe or long-term complications and improve overall outcomes. Pandemic awareness, increasing global incidence, fatal illness effects, expenses associated with outbreaks, reducing suffering, and other broader implications highlight the study’s wider significance. Drug design as a novel treatment approach to reduce the risk of resistance to the virus resulting from overuse of broad-spectrum antiviral therapies for unrelated viral diseases has been evaluated using computational techniques. Initially, molecular docking targeted the envelope glycoprotein of the WNV, utilizing a set of 5375 phytochemicals found in the IMPPAT database. Their binding affinities were −7.464, −5.802, −5.617, and −4.92, kcal/mol for CID: 359 (Phloroglucinol), 9064 (Cianidanol), 25310 (L-Rhamnose), and 492405 (Favipiravir), respectively. The lead compounds and the control ligand both bind at the common active site of the macro-molecule, as evidenced by their interactions with the same amino acid residues at LEU281, ASN47, THR282, SER29, MET48, MET46, and MET45, correspondingly. In post-docking MM-GBSA the negative binding energy of the P-L complex for the compounds CIDs: 359, 9064, 25310, and 492405 (control) were −29.16, −33.45, −32.02, and −3.16 kcal/mol, correspondingly. The selected compounds are secure and efficient since they demonstrate excellent toxicological and Pk characteristics. The compounds were further evaluated to confirm their stability and binding affinity to the target protein by molecular dynamics simulation (RMSD, RMSF, Rg, SASA, H-bond, P-L, and L-P contact). Following this, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM) studies were conducted using the MD trajectory data. The ligands evaluated in this study demonstrated considerable stability of the proteins’ binding site when complexed with CID: 9064 and CID: 25310, respectively, in the MD simulation, which also revealed a high negative binding free energy value, indicating a robust interaction between the target and lead compounds. The three principal components (PC1, PC2, PC3) for the lead compounds corresponding to CID: 9064 (40.37%, 23.02%, and 8.82%) and CID: 25310 (73.04%, 10.06%, and 3.77%), respectively, indicate that their complexes are more stable than the other L-P complexes. Consequently, both the compounds derived from the plants Tamarindus indica and Plantago ovate, respectively, may potentially impede the viral activity of the WNV envelope glycoprotein, indicating the possibility of these compounds as prospective phytochemical therapeutic candidates. This preclinical study can be used in further drug development processes, including in vivo studies and animal trials.

Exploring the chemical composition and processes of submicron aerosols in Delhi using aerosol chemical speciation monitor driven factor analysis

Abstract Wintertime non-refractory submicron particulate matter (NR-PM 1 ) species were measured in Delhi with an Aerodyne Aerosol Chemical Speciation Monitor (ACSM) during February–March 2018. The average NR-PM 1 mass concentration throughout the study was 58.0 ± 42.6 µg m −3 , where the contribution of organic aerosol (OA) was 69% of the total NR-PM 1 . In Delhi, chloride (10%) was the main inorganic contributor, followed by ammonium (8%), sulfate (7%), and nitrate (6%), contrasting with the prevalence of sulfate in most urban environments. Source apportionment analysis of the OA identified five major factors, including three primary contributors: hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-related OA (COA) and two secondary contributors: oxygenated primary OA (OPOA), and more-oxidized oxygenated OA (MO-OOA). A 19% rise in OPOA concentration was observed during high chloride episodes, suggesting the potential role of chloride in the atmospheric chemical transformation of OA. Traffic emissions significantly contribute to ambient OA, accounting for at least 41% of the total OA mass. Furthermore, the OA exhibited low oxidation levels regardless of its source. The f 44 : f 43 analysis revealed slower atmospheric oxidization of OA compared to other urban locations worldwide. Further investigations, including chamber experiments tailored to the Delhi atmosphere, are necessary to elucidate the atmospheric oxidants and the genesis of secondary OA alongside primary emissions.

Multisensory gamma stimulation enhances adult neurogenesis and improves cognitive function in male mice with Down Syndrome

Generalized spatial modulation for underwater backscatter communication using acoustic metasurfaces

Examining wage inequality among women in India: A multidimensional analysis of socio-economic disparities

Using the nationally representative Indian Human Development Surveys 2004–05 and 2011–12 and multiple inequality measures/frameworks, we investigate both vertical (within-group/interpersonal) and horizontal (between-group/inter group) socioeconomic (based on caste, religion, location and region) inequalities in wages among women in India. We find that the wage inequality (WI) is extremely high (around 60%) and has increased during 2004–12 driven by within-group inequalities which are very high and have increased, whereas between-group inequalities have reduced. There are stark rural-urban divides be it wage labour participation or mean wages; at the same time the WI itself is substantially higher in urban areas. Caste-based WIs are enormous with women belonging to scheduled groups and other backward castes earning substantially lower than their “upper” caste counterparts. The wages of Muslim women are consistently lower than women from other religions. There are vast inter-regional WIs, with the regions of Central and East having lower wages but higher inequalities.

Development of COPMAN-Air method for high-sensitivity detection of SARS-CoV-2 in air

Abstract Several studies have successfully detected SARS-CoV-2 in air samples. However, most of these studies focused on validating the air collection method, and there was no report on the development of a virus detection method. In this study, to detect viruses in air samples with greater sensitively than conventional detection methods, we utilized COPMAN, a highly sensitive virus detection method originally used for wastewater samples. We applied COPMAN to air samples, thereby developing COPMAN-Air. Briefly, this method efficiently detects the extremely low levels of viral RNA in air samples via three reaction steps: RT, preamplification, and qPCR, as it is performed with COPMAN. We evaluated COPMAN-Air using samples from a fever clinic for COVID-19 patients. COPMAN-Air demonstrated a higher detection rate of viral RNA compared with conventional methods, detecting the virus in 22 out of 23 samples (95.7%) vs. 14 out of 23 samples (60.9%). Additionally, a positive correlation ( r  = 0.70) was detected between the amount of viral RNA detected by COPMAN-Air and the number of confirmed COVID-19 cases, suggesting that COPMAN-Air could estimate the number of SARS-CoV-2-positive individuals in a given space based on the quantitative values of SARS-CoV-2 RNA in air samples. Surveillance systems for airborne pathogens using COPMAN-Air are expected to be valuable for estimating the number of infected individuals and for guiding the implementation of public health measures.

Membrane Charge Drives the Aggregation of TDP-43 Pathological Fragments

Driving Electrochemical Organic Hydrogenations on Metal Catalysts by Tailoring Hydrogen Surface Coverages

Electrocatalytic Ethylene Glycol to Long-Chain C₃₊ α-Hydroxycarboxylic Acids via Cross-Coupling with Primary Alcohols

Evidence of a Three-State Mechanism in DNA Hairpin Folding

Combination of skin sympathetic nerve activity and urine biomarkers in improving diagnostic accuracy for urge urinary incontinence

Effect of electrical grade glass fibres and silver nanoparticles on the mechanical properties of provisional PMMA material

Abstract The use of provisional crowns and bridges rendered the necessary care for the prepared teeth. The protection of the prepared tooth is one of the most important factors in the long-term success of fixed dental prosthesis. Temporary crowns and bridges for longer periods of use are most often made of acrylic material. Unfortunately, it does not have the appropriate mechanical properties or resistance to microbial colonization Therefore, the purpose is to modify it by adding glass fibers (2% w/w) and silver nanoparticles (0.5% w/w). A total of 160 samples were prepared which were segregated into 4 groups based on the test performed. Each group had 4 subgroups which consisted of samples containing silver nanoparticles (0.5% w/w) and E-glass fiber (2% w/w) mixed with PMMA in their respective concentrations. The samples were then tested for surface roughness, micro-hardness, flexural strength and SEM Analysis. The results of the study showed that there were no effect on the surface roughness values after incorporating silver nanoparticles and E-glass fibers. However, samples containing silver nanoparticles and E-Glass fibre individually had higher values of microhardness and flexural strength than those who had both together. The SEM images showed clumping of silver nanoparticles non-uniform orientation of E-glass fibers. Thus, it can be concluded that silver nanoparticles and E-glass fibre when added separately to PMMA, enhance its mechanical properties. However, better methods of mixing PMMA with silver particles and glass fibers is needed to attain a uniform distribution. Further, the orientation of E-glass fibers could also have an effect on the flexural properties of PMMA.

Estimation of risk perception of mine workers in underground metalliferous mines using multivariate structural equation modelling

A lightweight encryption algorithm for resource-constrained IoT devices using quantum and chaotic techniques with metaheuristic optimization

Discovery of an ApoE4-targeted small-molecule SirT1 enhancer for the treatment of Alzheimer’s disease

Abstract Decreased expression of sirtuin 1 (SirT1) has been implicated in Alzheimer’s disease (AD), and as we previously reported, is related to transcriptional repression by the major risk factor for sporadic AD, apolipoprotein E4 (ApoE4). Herein we describe the discovery of an orally brain-permeable small-molecule, DDL-218, that enhanced SirT1 in ApoE4-expressing neuronal cells and a murine AD model. DDL-218 increased the transcription factor NFYb resulting in upregulation of PRMT5. Mechanistic and modeling studies show that binding of ApoE4 to the SirT1 gene promoter can be displaced by PRMT5 leading to increased SirT1 transcription. DDL-218 treatment elicited improvement in memory in the AD model, suggesting that DDL-218 enhancement of neurotrophic SirT1 in the brain has potential to modulate neuronal activity that may clinically provide an improvement in cognitive function and complement the current anti-Aβ antibody monotherapy. Our findings support further development of DDL-218 as a novel ApoE4-targeted therapeutic candidate for AD.

Intelligent identification method of origin for Alismatis Rhizoma based on image and machine learning

Abstract Alismatis Rhizoma (AR) is widely utilized as a natural medicine across many Asian countries. However, in China, due to its complex origins, AR quality varies, which can affect clinical efficacy. Therefore, there is a need for a method that is both fast and objective to determine the source of AR. In this study, a total of 400 samples of two species and four geographic origins from AR were imaged and processed. From these images, 17 features were extracted, including three shape (S), two color (C), and 12 texture features (T), resulting in a total of 6800 data points. Four commonly used classification models Random Forest (RF), Extreme Learning Machine (ELM), Back Propagation (BP) neural network, and Support Vector Machines (SVM) were tested to find the optimal combination of AR fusion features and classification models. The S + T-RF combinations achieved the best results, with 99.17% accuracy in two species identification and 96.67% accuracy in four geographic origin identification on test sets. These results suggest that image processing combined with the RF model can quickly and effectively identify the complex origins of AR and can provide a reference for the origins identification of other natural medicines.

Investigating the protective effect of hydroxylated fullerenes on cognitive function in rats with temporal lobe epilepsy

Quantitative comparison of the performance of acoustic, optical and pressure sensors for pulse wave analysis

A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation

Abstract Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein’s native structure from its amino acid sequence, the focus on a single-structure view limits understanding of the dynamic nature of protein molecules. Acquiring a multi-structure view of protein molecules remains a broader challenge in computational structural biology. Alternative representations, such as distance matrices, offer a compact and effective way to explore and generate realistic tertiary protein structures. This paper presents TP-VWGAN, a hybrid model to improve the realism of generating distance matrix representations of tertiary protein structures. The model integrates the probabilistic representation learning of the Variational Autoencoder (VAE) with the realistic data generation strength of the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). The main modification of TP-VWGAN is incorporating residual blocks into its VAE architecture to improve its performance. The experimental results show that TP-VWGAN with and without residual blocks outperforms existing methods in generating realistic protein structures, but incorporating residual blocks enhances its ability to capture key structural features. Comparisons also demonstrate that the more accurately a model learns symmetry features in the generated distance matrices, the better it captures key structural features, as demonstrated through benchmarking against existing methods. This work moves us closer to more advanced deep generative models that can explore a broader range of protein structures and be applied to drug design and protein engineering. The code and data are available at https://github.com/aalaa-sehsah/tp-vwgan.

A highly sensitive Anti-Müllerian hormone test as a promising tool for follicle growth prediction in primary ovarian insufficiency patients

Abstract Primary ovarian insufficiency (POI) patients often require prolonged stimulation for follicular growth. Anti-Müllerian hormone (AMH), produced by granulosa cells of early-stage follicles, is a potential a biomarker for predicting follicular development in POI patients undergoing ovarian stimulation. This retrospective study analyzed 165 patients undergoing 504 long controlled ovarian stimulation cycles. AMH levels were measured three weeks after stimulation initiation using a highly sensitive assay to guide decisions on extending stimulation beyond four weeks. Follicular development occurred in 9.7% of cycles among 41 patients, who had shorter amenorrhea durations and lower baseline follicle-stimulating hormone levels. Three-week AMH levels showed superior predictive ability for follicular development (area under the curve: 0.957; optimal threshold: 2.45 pg/ml) and were negatively correlated with time to follicular detection (R = − 0.326, P < 0.05). However, AMH levels did not significantly affect the precise time required for follicular development or show significant differences in oocyte yield or embryo quality. The study concludes that three-week AMH levels can predict follicular growth in POI patients. These findings suggest that a highly sensitive AMH assay could be a valuable tool for guiding ovarian stimulation in POI patients, potentially improving treatment outcomes.