Screening Properties Research Articles

Anticancer activity and other biomedical properties of β-sitosterol: Bridging phytochemistry and current pharmacological evidence for future translational approaches.

Sterols, including β-sitosterol, are essential components of cellular membranes in both plant and animal cells. Despite being a major phytosterol in various plant materials, comprehensive scientific knowledge regarding the properties of β-sitosterol and its potential applications is essential for scholarly pursuits and utilization purposes. β-sitosterol shares similar chemical characteristics with cholesterol and exhibits several pharmacological activities without major toxicity. This study aims to bridge the gap between phytochemistry and current pharmacological evidence of β-sitosterol, focusing on its anticancer activity and other biomedical properties. The goal is to provide a comprehensive understanding of β-sitosterol's potential for future translational approaches. A thorough examination of the literature was conducted to gather relevant information on the biological properties of β-sitosterol, particularly its anticancer therapeutic potential. Various databases were searched, including PubMed/MedLine, Scopus, Google Scholar, and Web of Science using appropriate keywords. Studies investigating the effects of β-sitosterol on different types of cancer were analyzed, focusing on mechanisms of action, pharmacological screening, and chemosensitizing properties. Modern pharmacological screening studies have revealed the potential anticancer therapeutic properties of β-sitosterol against various types of cancer, including leukemia, lung, stomach, breast, colon, ovarian, and prostate cancer. β-sitosterol has demonstrated chemosensitizing effects on cancer cells, interfering with multiple cell signaling pathways involved in proliferation, cell cycle arrest, apoptosis, survival, metastasis invasion, angiogenesis, and inflammation. Structural derivatives of β-sitosterol have also shown anti-cancer effects. However, research in the field of drug delivery and the detailed mode of action of β-sitosterol-mediated anticancer activities remains limited. β-sitosterol, as a non-toxic compound with significant pharmacological potential, exhibits promising anticancer effects against various cancer types. Despite being relatively less potent than conventional cancer chemotherapeutics, β-sitosterol holds potential as a safe and effective nutraceutical against cancer. Further comprehensive studies are recommended to explore the biological properties of β-sitosterol, including its mode of action, and develop novel formulations for its potential use in cancer treatment. This review provides a foundation for future investigations and highlights the need for further research on β-sitosterol as a potent superfood in combating cancer.

Psychometric properties of the Global Psychotrauma Screen in the United States

ABSTRACT Background Prior research assessing the psychometric properties of the Global Psychotrauma Screen provided support for its internal consistency reliability, construct validity, convergent validity, and divergent validity in several international samples, but not specifically in a U.S. subsample. Objective The purpose of this study was to assess psychometric properties of the GPS in the U.S. Method This observational study included a convenience sample of individually recruited participants (N = 231) who completed an initial study with 126-item online questionnaire and a two-week follow-up study with GPS alone through the weblinks provided by the research team. Data analyzes included measuring internal consistency and test–retest reliability, exploratory and confirmatory factor analyzes (EFA and CFA), convergent and divergent validity, sensitivity, specificity, and severity of the GPS symptom items. Additional CFA was conducted with data (N = 947) from the GPS multinational research project, U.S. subsample. Results The results showed acceptable internal consistency and test–retest reliability, convergent validity, and divergent validity of the GPS. The construct validity results supported a three-factor structure of the GPS symptoms. The GPS domains showed acceptable sensitivity and specificity with the cut-off scores of 3 for PTSD and 5 for CPTSD domains; and the scores of 1 for the anxiety, depression, and insomnia domains respectively. The GPS risk factors predicted the GPS symptom severity. Conclusions This study provides new and additional evidence on the psychometric properties of the GPS which may help health care providers with the selection of an appropriate screening instrument for trauma-related transdiagnostic symptoms. The study limitations should be addressed in future research through the replication of EFA and CFA internationally with larger samples, and the inclusion of a reference standard for dissociation.

Voice Handicap Index: Cut-Off Points for Screening European Portuguese With Voice Disorders

To determine cut-off values of the Voice Handicap Index (VHI) and the shortened version (VHI-10) for European Portuguese (EP) with voice disorders. In addition, to analyze the discriminatory power of individual items of the VHI-10 and the ability to detect differences in various Ear Nose and Throat (ENT) diagnoses. Cross-sectional cohort study. All participants completed the EP VHI and a 4-point self-assessment of voice disorder severity. The case group (subjects with voice disorders) underwent assessment through strobovideolaryngoscopy examinations by ENT surgeons and perceptual analyses by speech-language pathologists (SLPs). In contrast, the control group was evaluated solely by SLPs. Data were analyzed using a receiver-operating characteristic curve to determine the accuracy and cut-off values of the VHI and VHI-10. The study involved a sample of 350 adults (171 cases and 179 controls), predominantly women aged 18-88years. The area under curve (AUC) for VHI and VHI-10 was 0.997 [95% confidence interval (CI): 0.992-1] and 0.998 [95% CI: 0.995-0.999], respectively. Optimal cut-off values were identified as 13.5 for VHI (0.994 sensitivity and 0.989 specificity) and 5.5 for VHI-10 (0.977 sensitivity and 0.955 specificity). Each individual item within the VHI-10 significantly contributed to the overall assessment, exhibiting varying discriminatory power ranging from excellent (AUC=0.937) to poor (AUC=0.637). Significant differences were found in the case group between neurogenic disorders and healthy larynx (P=0.014), structural and physiologic minor laryngeal abnormalities (P=0.006), and inflammatory disorders (P=0.043). The VHI and the VHI-10 exhibited accurate screening properties for predicting EP speakers with voice disorders.

Preliminary Phytochemical screening and Antioxidant properties of Methanolic root extract of Picrorhiza kurroa

Objective: Picrorhiza kurroa Royle ex Benth. (Scrophulariacae) is a herb in the Ayurvedic system of medicine highly noted for its hepatoprotective effect. Over exploitation for various medicinal purposes has made the rhizome as an endangered species. In spite of its geographical distribution, it is available in all the herbal drug stores. The rationale behind this study is to authenticate and standardize the Picrosides by different parameters to enable its identity, purity and efficacy. Thus, the Present study analyzed the presence of phytochemicals qualitatively and evaluated the antioxidant properties in the methanolic extracts of Picrorhiza kurroa. Methods: Preliminary analysis of phytoconstituents of root parts of this plant using the standard procedures and Antioxidant properties were determined by 1,1- diphenyl,2-picrylhydrazyl (DPPH) assay. Results: The qualitative phytochemical analysis revealed the presence of secondary active constituent’s such as glycosides, cardiacglycosides, Tannins, terpenoids, steroids and coumarins and also possesses high free radical scavenging property. Conclusion: Therefore this study explored the antioxidant properties which strongly supports that could prevent many free radical related disorders. Also paves the way for further quantitative and animal studies, planned to explore the molecular mechanisms for the study based on cardiovascular disorders.

Synthesis, Characterization, DNA Synergy and Molecular Docking Studies of Novel 1,10-Phenanthroline Derived Mixed Ligand Metal(II) Complexes

The effect of tumour and all its related costs, both human and commercial factor is driving the exploration for novel curative medicines.Although platinum-based anticancer drugs have shown considerable success, their use is complicated by a number of drawbacks. Therefore, this study is mainly designed for transition metal complex based chemotherapeutic agents to reduce the obstacles for platinum-based anticancer agents presently used. Herein, novel mixed ligand-metal(II) complexes derived from 9-anthraldehyde, 2-aminophenol, metal chlorides and 1,10-phenanthroline (co-ligand) were synthesized and characterized using several spectral techniques, which revealed that they possess an octahedral structure. The interaction of calf thymus DNA (CT-DNA) with synthesized compounds confirmed that the compounds are binded with DNA via intercalation. The mode of interaction was affirmed by the docking simulations, where the synthesized compounds were docked with EGFR protein and 1B-DNA through intercalation. The biological screening results revealed that the synthesized complexes exhibited superior anti-pathogenic screening properties compared to ligands. The proliferation resistance against cancer cells and free radical scavenger studies of prepared compounds indicated that the presence of heterocyclic ligand is an important factor for boosting the anticancer potential and radical scavenger skill.

Phytochemical Screening and Antibacterial Properties of Methanol, Hexane and Water Extracts of Clinacanthus nutans

Phytochemical Screening and Antibacterial Properties of Methanol, Hexane and Water Extracts of Clinacanthus nutans

Does the degree of polarization of vector beams remain unchanged on atmospheric propagation?

All roads lead to Rome. In this article we propose a novel theoretical framework to demonstrate vector beams whose degree of polarization does not change on atmospheric propagation. Inspired by the Fresnel equations, we derive the reflected and refracted field of vector beams propagating through a phase screen by employing the continuity of electromagnetic field. We generalize the conventional split-step beam propagation method by considering the vectorial properties in the vacuum diffraction and the refractive properties of a single phase screen. Based on this vectorial propagation model, we extensively calculate the change of degree of polarization (DOP) of vector beams under different beam parameters and turbulence parameters both in free-space and satellite-mediated links. Our result is that whatever in the free-space or satellite-mediated regime, the change of DOP mainly fluctuates around the order of 10-13 to 10-6, which is almost negligible.

Density functional applications of jellium with a local gap model correlation energy functional.

We develop a realistic density functional approximation for the local gap, which is based on a semilocal indicator that shows good screening properties. The local band model has remarkable density scaling behaviors and works properly for the helium isoelectronic series for the atoms of the Periodic Table, as well as for the non-relativistic noble atom series (up to 2022 e-). Due to these desirable properties, we implement the local gap model in the jellium-with-gap correlation energy, developing the local-density-approximation-with-gap correlation functional (named LDAg) that correctly gives correlation energies of atoms comparable with the LDA ones but shows an improvement for ionization potential of atoms and molecules. Thus, LDAg seems to be an interesting and useful tool in density functional theory.

Adjusted feature screening for ultra-high dimensional missing response

ABSTRACT This paper presents a new method for the feature screening of ultra-high dimensional data with response missing at random. The distribution function of the missing response is completed by imputation technology, and then the distance correlation between the imputed distribution function of response and the distribution function of covariate is used as an index for feature screening. The proposed method has the following advantages. First, it is a nonparametric model-free method, and can detect the nonlinear relationship between variables. Second, it is robust to covariates with outliers and heavy-tailed distributions. Third, it can deal with multi-dimensional response variables directly. Under certain assumptions, this paper demonstrates the sure screening and ranking consistency properties. Simulation studies are conducted to examine the performance of the proposed procedure and to compare with existing methods. Finally, our method is applied to the data analysis of diffuse large B-cell lymphoma.

Planning the Optimal Reference Flight Path of an Aircraft Using a Terrain Map

In connection with the development of aviation technology, the expansion of the functionality of modern aircraft and the complication of the tasks being solved, additional stringent requirements are imposed on the accuracy of air navigation. Along with high accuracy, navigation autonomy is also required. The autonomy of aircraft navigation implies the navigation of an aircraft without the use of active radar facilities. In this article, to calculate the optimal reference flight path of an aircraft in a horizontal plane, the Bellman dynamic programming method is proposed using a digital terrain map. A technique, algorithms have been developed, and a numerical experiment has been carried out using fragments of a digital terrain map. High accuracy and autonomy of navigation are necessary, among other things, to ensure stealth (invisibility) of aircraft by ground-based radar facilities of a potential enemy in low-altitude flight mode. The best stealth of an aircraft flight in a low-altitude flight mode is achieved not only by flying around, but also by avoiding obstacles, due to the shielding properties of the earth’s surface. Autonomy of navigation provides periodic correction of the reference flight path during long-term flight, and high accuracy prevents aircraft collisions with the earth’s surface in low-altitude flight mode. The purpose of this work is to select the optimal reference trajectory (route) of an aircraft flight in the low-altitude flight mode. This article discusses a method developed based on the principle of dynamic programming for calculating the reference flight path in the horizontal plane depending on the DEM and the implementation of the algorithm based on the proposed method with a specific numerical example. The method makes it possible to calculate the optimal flight route, which provides the greatest secrecy of the aircraft in the low-altitude flight mode using the screening properties of the earth’s surface irregularities.

Screening Properties of Pure Water and Dilute Aqueous Solutions

Screening Properties of Pure Water and Dilute Aqueous Solutions

Application of Machine Learning in Material Synthesis and Property Prediction.

Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.

Identification of potential phytochemicals and their inhibitory effect on the PERK receptor mediated UPR pathway for neuronal disease regulation: an in silico insight

The endoplasmic reticulum (ER) has been considered as the key site of protein biosynthesis and maturation in the eukaryotic cell. In recent years, the sequence at the N-terminal region of translated protein has shown a particular emphasis as a signal responsible for site-specific translocation mediated by post-translational modification. Once the native conformation is not achieved, the degradation pathway is activated, and therefore the restoration of the homeostasis of ER function in UPR pathway is initiated. One of the transmembrane proteins, PKR-like ER kinase (PERK) plays a key role in the activation of UPR through the inhibition of the translation process, thus preventing the cells from apoptosis due to chronic ER stress. Dysregulation of the neuronal proteostasis often results in neuronal dysfunction and its crucially associated neurodegenerative diseases or its manifestation of neuropathic pain. The correlation between ER stress and its associated signaling cascade, namely UPR, is well established in context of neuropathological modifications. This furthermore suggests that the proteins of the signaling cascade such as PERK can serve as a potential target during the onset of neuronal damage. The aim of this study was to identify the potential phytocompounds by evaluating the physicochemical properties, Lipinski screening, ADMET and toxicity properties of the selected phytocompounds by using SwissADME, MolInspiration and pKCSM webservers, which could establish a comparatively better affinity and binding energy than the control drug as GSK2606414 in set up the treatment of the neuronal diseases through molecular docking via PyRx and validating their structural stability through simulation using the Sybyl software for over100ns. Communicated by Ramaswamy H. Sarma

Feature screening for multiple responses

Feature screening has been widely investigated in many literatures and quite a few procedures have been proposed. However, most of the existing methods are developed based on regularization strategy and model assumptions such as linear model and Gaussian distribution, which limit their application range. And also, they were mainly designed to deal with univariate response and cannot handle multiple responses situations. To tackle these issues, we introduce a new association measure for multiple responses and univariate predictor, called multiple explained variability (MEV), and further propose a feature screening procedure, named MEV-SIS, based on MEV. MEV-SIS removes the commonly used model assumptions and can conduct feature screening for multiple responses simultaneously. The asymptotic properties of MEV are deduced, and the sure screening property and ranking consistency property of MEV-SIS are obtained. Extensive simulation studies and real data application demonstrate the advantage of MEV-SIS over the existing screening procedures in sufficiency and robustness.

Wasserstein filter for variable screening in binary classification in the reproducing kernel Hilbert space

The aim of this paper is to develop a marginal screening method for variable screening in high-dimensional binary classification based on the Wasserstein distance accounting for the distributional difference. Many existing screening methods, such as the two-sample t-test and Kolmogorov test, have been developed under the parametric/nonparametric modeling assumptions to reduce the dimension of the predictors. However, such modeling specifications or nonparametric approaches are associated with the probability measure induced by the predictor in a Euclidean space. While many machine learning methods have successfully found the nonlinear decision boundary in the transformed space, called the reproducing kernel Hilbert space (RKHS), we consider the Wasserstein filter's capacity to detect the distributional difference between two probability measures induced by the nonlinear function of the predictor in the RKHS. Thereby, we can flexibly filter out the non-informative predictors associated with the binary classification, as well as escape the modeling assumptions required in a Euclidean space. We prove that the Wasserstein filter satisfies the sure screening property under some mild conditions. We also demonstrate the advantages of our proposed approach by comparing the finite sample performance of it with those of the existing choices through simulation studies, as well as through application to lung cancer data.

Variable scintillation arcs of millisecond pulsars observed with the Large European Array for Pulsars

ABSTRACT We present the first large sample of scintillation arcs in millisecond pulsars (MSPs), analysing 12 sources observed with the Large European Array for Pulsars (LEAP), and the Effelsberg 100-m telescope. We estimate the delays from multipath propagation, measuring significant correlated changes in scattering time-scales over a 10 yr time span. Many sources show compact concentrations of power in the secondary spectrum, which in PSRs J0613−0200 and J1600−3053 can be tracked between observations, and are consistent with compact scattering at fixed angular positions. Other sources such as PSRs J1643−1224 and J0621+1002 show diffuse, asymmetric arcs which are likely related to phase-gradients across the scattering screen. PSR B1937+21 shows at least three distinct screens which dominate at different times and evidence of varying screen axes or multiscreen interactions. We model annual and orbital arc curvature variations in PSR J0613−0200, providing a measurement of the longitude of ascending node, resolving the sense of the orbital inclination, where our best-fit model is of a screen with variable axis of anisotropy over time, corresponding to changes in the scattering of the source. Unmodelled variations of the screen’s axis of anisotropy are likely to be a limiting factor in determining orbital parameters with scintillation, requiring careful consideration of variable screen properties, or independent very long baseline interferometry (VLBI) measurements. Long-term scintillation studies such as this serve as a complementary tool to pulsar timing, to measure a source of correlated noise for pulsar timing arrays, solve pulsar orbits, and to understand the astrophysical origin of scattering screens.

Model-free, monotone invariant and computationally efficient feature screening with data-adaptive threshold

Feature screening for ultrahigh-dimensional data, in general, proceeds with two essential steps. The first step is measuring and ranking the marginal dependence between response and covariates, and the second is determining the threshold. We develop a new screening procedure, called SIT-BY procedure, that possesses appealing statistical properties in both steps. By employing sliced independence estimates in the measuring and ranking stage, our proposed procedure requires no model assumptions, remains invariant to monotone transformation, and achieves almost linear computation complexity. Inspired by false discovery rate (FDR) control procedures, we offer a data-adaptive threshold benefit from the asymptotic normality of test statistics. Under moderate conditions, we demonstrate that our procedure can asymptotically control the FDR while maintaining the sure screening property. We investigate the finite sample performance of our proposed procedure via extensive simulations and an application to genome-wide dataset.

Quantile forward regression for high-dimensional survival data.

Despite the urgent need for an effective prediction model tailored to individual interests, existing models have mainly been developed for the mean outcome, targeting average people. Additionally, the direction and magnitude of covariates' effects on the mean outcome may not hold across different quantiles of the outcome distribution. To accommodate the heterogeneous characteristics of covariates and provide a flexible risk model, we propose a quantile forward regression model for high-dimensional survival data. Our method selects variables by maximizing the likelihood of the asymmetric Laplace distribution (ALD) and derives the final model based on the extended Bayesian Information Criterion (EBIC). We demonstrate that the proposed method enjoys a sure screening property and selection consistency. We apply it to the national health survey dataset to show the advantages of a quantile-specific prediction model. Finally, we discuss potential extensions of our approach, including the nonlinear model and the globally concerned quantile regression coefficients model.

A new covariate selection strategy for high dimensional data in causal effect estimation with multivariate treatments

Selection of covariates is crucial in the estimation of average treatment effects given observational data with high or even ultra-high dimensional pretreatment variables. Existing methods for this problem typically assume sparse linear models for both outcome and univariate treatment, and cannot handle situations with ultra-high dimensional covariates. In this paper, we propose a new covariate selection strategy called double screening prior adaptive lasso (DSPAL) to select confounders and predictors of the outcome for multivariate treatments, which combines the adaptive lasso method with the marginal conditional (in)dependence prior information to select target covariates, in order to eliminate confounding bias and improve statistical efficiency. The distinctive features of our proposal are that it can be applied to high-dimensional or even ultra-high dimensional covariates for multivariate treatments, and can deal with the cases of both parametric and nonparametric outcome models, which makes it more robust compared to other methods. Our theoretical analyses show that the proposed procedure enjoys the sure screening property, the ranking consistency property and the variable selection consistency. Through a simulation study, we demonstrate that the proposed approach selects all confounders and predictors consistently and estimates the multivariate treatment effects with smaller bias and mean squared error compared to several alternatives under various scenarios. In real data analysis, the method is applied to estimate the causal effect of a three-dimensional continuous environmental treatment on cholesterol level and enlightening results are obtained.

Numerical Modelling of High-Speed Loading of Periodic Interpenetrating Heterogeneous Media with Adapted Mesostructure

A series of calculations has been conducted to study the high-speed interaction of space debris (SD) particles with screens of finite thickness. For the first time, taking into account the fracture effects, a numerical solution has been obtained for the problem of high-velocity interaction between SD particles and a volumetrically reinforced penetrating composite screen. The calculations were performed using the REACTOR 3D software package in a three-dimensional setup. To calibrate the material properties of homogeneous screens made of aluminum alloy A356, stainless steel 316L, and multilayer screens, methodical load calculations were carried out. The properties of materials have been verified based on experimental data through systematic calculations of the load on homogeneous screens made of aluminum alloy A356, stainless steel 316L, and multilayer screens comprising a combination of aluminum and steel plates. Several options for the numerical design of heterogeneous screens based on A356 and 316L were considered, including interpenetrating reinforcement with steel inclusions and a gradient distribution of steel throughout the thickness of an aluminum matrix. The study has revealed that the screens constructed as a two-layer composite of A356/316L, volumetrically reinforced composite screens, and heterogeneous screens with a direct gradient distribution of steel in the aluminum matrix provide protection for devices from both a single SD particle and streams of SD particles moving at speeds of up to 6 km/s. SD particles were modeled as spherical particles with a diameter of 1.9 mm made of the aluminum alloy Al2017-T4 with a mass of 10 mg.