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Curcumin, a polyphenol with a rich history spanning two centuries, has emerged as a promising therapeutic agent targeting multiple signaling pathways and exhibiting cellular-level activities that contribute to its diverse health benefits. Extensive preclinical and clinical studies have demonstrated its ability to enhance the therapeutic potential of various bioactive compounds. While its reported therapeutic advantages are manifold, predominantly attributed to its antioxidant and anti-inflammatory properties, its efficacy is hindered by poor bioavailability stemming from inadequate absorption, rapid metabolism, and elimination. To address this challenge, nanodelivery systems have emerged as a promising approach, offering enhanced solubility, biocompatibility, and therapeutic effects for curcumin. We have analyzed the knowledge on curcumin nanoencapsulation and its synergistic effects with other compounds, extracted from electronic databases. We discuss the pharmacokinetic profile of curcumin, current advancements in nanoencapsulation techniques, and the combined effects of curcumin with other agents across various disorders. By unifying existing knowledge, this analysis intends to provide insights into the potential of nanoencapsulation technologies to overcome constraints associated with curcumin treatments, emphasizing the importance of combinatorial approaches in improving therapeutic efficacy. Finally, this compilation of study data aims to inform and inspire future research into encapsulating drugs with poor pharmacokinetic characteristics and investigating innovative drug combinations to improve bioavailability and therapeutic outcomes.

ABSTRACT Aim: The present study was undertaken to evaluate the performance of different algorithms for flattening filter-free (FFF) and flattened (FF) photon beams in three different in-homogeneities. Materials and Method: Computed tomography (CT) image sets of the CIRS phantom maintained in the SAD setup by placing the ionization chamber in the lung, bone, and tissue regions, respectively, were acquired. The treatment planning system (TPS) calculated and the ionization chamber measured the doses at the center of the chamber (in the three mediums) were recorded for the flattened and non-flattened photon beams. Results: The results were reported for photon energies of 6 MV, 10 MV, 15 MV, 6 FFF, and 10 FFF of field sizes 5 × 5 cm2, 10 × 10 cm2, and 15 × 15 cm2. In the bone inhomogeneity, the pencil beam algorithm predicted that the maximum dose variation was 4.88% of measured chamber dose in 10-MV photon energy for the field size 10 × 10 cm2. In water inhomogeneity, both the collapsed cone and Monte Carlo algorithm predicted that the maximum dose variation was ± 3% of measured chamber dose in 10-MV photon energy for the field size 10 × 10 cm2 and in 10-MV FFF photon energy for the field size 5 × 5 cm2, whereas in lung inhomogeneity, the pencil beam algorithm predicted that the highest dose variation was - 6.9% of measured chamber dose in 10-MV FFF photon energy for the field size 5 × 5 cm2. Conclusion: FF and FFF beams performed differently in lung, water, and bone mediums. The assessment of algorithms was conducted using the anthropomorphic phantom; therefore, these findings may help in the selection of appropriate algorithms for particular clinical settings in radiation delivery.

Turing instability plays a central role in the emergence of complex patterns in biological and physical systems. In this work, we present a novel framework that reveals how both convergent and divergent network dynamics can trigger previously undetectable Turing instabilities. By analyzing the eigenvalue distribution of the Laplacian matrix and its pseudo-inverse, we derive general conditions under which such instabilities arise. Applying this to the Hindmarsh–Rose neuronal model, we uncover how subtle changes in network topology, diffusion, and divergent velocity can lead to transitions between stable, periodic, and chaotic states-corresponding to seizure-free and epileptic dynamics. Our findings offer a deeper understanding of the mechanisms underlying pattern formation and seizure occurrence, with potential implications for neurological modeling and intervention strategies.

Endogenous androgens are pivotal in the development and progression of prostate cancer (PC). We investigated nanoparticle formulations of curcumin and piperine in modulating steroidogenesis within PC cells. Using multiple PC cell lines (LNCaP, VCaP, DU145 and PC3) we studied the effects of curcumin, piperine, and their nanoparticle formulations—curcumin nanoparticles, piperine nanoparticles, and curcumin–piperine nanoparticles (CPN)—on cell viability, migration, and steroid biosynthesis. Curcumin and its nanoparticle formulations significantly reduced cell viability in PC cells, with curcumin–piperine nanoparticles showing the highest efficacy. These treatments also inhibited cell migration, with CPN exhibiting the most pronounced effect. In assays for steroid biosynthesis, curcumin, and its nanoparticle formulations, as well as piperine and its nanoparticles, selectively inhibited 17α-hydroxylase and 17,20-lyase activities of cytochrome P450 17A1 (CYP17A1). Abiraterone, a CYP17A1 inhibitor, displayed a broader inhibition of steroid metabolism including cytochrome P450 21-hydroxylase activity, whereas curcumin and piperine provided a more targeted inhibition profile. Analysis of steroid metabolites by liquid chromatography-mass spectrometry revealed that CPN caused significant reduction of androstenedione and cortisol, suggesting potential synergistic effects. In conclusion, nanoformulations co-loaded with curcumin and piperine offer an effective approach to targeting steroidogenesis and could be promising candidates for therapies aimed at managing androgen-dependent PC.

A tunable single-/dual-band notch filter is proposed for THz applications. The filter geometry contains a proximity-coupled graphene patch. The filter structure operates with propagating- and nonpropagating-type transverse magnetic (\\[ TM \\]) modes. Different higher-order \\[ TM_mn \\] (where \\[ m \\] and \\[ n \\] are integers) modes can be excited in the filter structure by changing the aspect ratio of the graphene patch, thus enabling the filter response to be tuned to obtain single- or dual-band notch characteristics. Appropriate selection of the physical parameters of the filter structure allows the desired response at different frequencies to be obtained. Furthermore, the response of the proposed band notch filter can be tuned over frequency by changing the chemical potential of the graphene.

The world faces Covid-19 waves, and the overall pattern of confirmed cases shows periodic oscillations. In this paper, we investigate the spatiotemporal spread of Covid-19 in the network-organized SIR model with an extrinsic incubation period of the driving factors. Firstly, Our analysis shows the occurrences of Hopf bifurcation and periodic outbreaks consistent with the actual spread of Covid-19. And we investigate periodic waves on spatial scales using Turing instability, and the spread of infected individuals increases the localized hot spots. We study the effect of the incubation period, and more incubation periods generate Turing instability resulting in periodic outbreaks. There is an occurrence of bursting states at peaks of periodic waves due to small diffusion of infected and susceptible, which means stable and unstable areas try to convert each other due to high competition among nodes. Also, We note the disappearance of these bursts when infected and susceptible individuals’ movements are easier; thus, the dominance of infected individuals prevails everywhere. Effective policy interventions and seasonality can cause periodic perturbations in the model, and therefore we study the impact of these perturbations on the spread of Covid-19. Periodic perturbations on the driving factors, infected individuals show co-existing spatial patterns. Chaotic outbreak becomes periodic outbreaks through alternating periodic or period-2 outbreaks as we regulate the amplitude and frequency of infected individuals. In short, regulations can erase period-2 and chaotic spread through policy interventions.

The design of microstrip patch (MP) antenna using Moth–Flame optimization (MFO) algorithm for UWB applications is presented in this article. MP antennas are designed to operate in dual and multi-band application as it possess the following advantages such as low cost, light weight and easy installation. To reduce the microstrip patch cross-polarized radiation and to attain the essential radiation parameters, the MP antenna is designed with a defected ground structure. The substrate of liquid crystal polymer is used here to reduce the material cost and the applicable geometry parameters are used to improve antenna performance. The MFO optimized antenna represents 50 mm × 50 mm compact size, which improves the performance of antenna. However, the simulation procedure is done by the MATLAB tool along with high frequency structure simulator for parameter optimization and performance analysis respectively. The operational bandwidth of the antenna is 3.1 GHz and the return loss is − 20 dB that covers the UWB (3.1–10.6 GHz) applications. The simulation outcomes exhibit good impedance bandwidth, radiation pattern, directivity, and relatively constant gain over the entire band of frequency comparing with the earlier methods. Finally, the proposed system can be a better option for the design of microstrip antenna in the communication system, to cover Bluetooth operations, Wi-Fi, Wi-MAX, Telemedicine and UWB applications.

Although γδ T cells are known to participate in immune dysregulation in solid tumors, their relevance to human microsatellite-stable (MSS) colorectal cancer (CRC) is still undefined. Here, using integrated gene expression analysis and T cell receptor sequencing, we characterized γδ T cells in MSS CRC, with a focus on Vδ1 + T cells. We identified Vδ1 + T cells with shared motifs in the third complementarity-determining region of the δ-chain, reflective of antigen recognition. Changes in gene and protein expression levels suggested a dysfunctional effector state of Vδ1 + T cells in MSS CRC, distinct from Vδ1 + T cells in microsatellite-instable (MSI). Interaction analysis highlighted an immunosuppressive role of fibroblasts in the dysregulation of Vδ1 + T cells in MSS CRC via the TIGIT-NECTIN2 axis. Blocking this pathway with a TIGIT antibody partially restored cytotoxicity of the dysfunctional Vδ1 phenotype. These results define an operative pathway in γδ T cells in MSS CRC. Although γδ T cells are known to participate in immune dysregulation in solid tumors, their relevance to human microsatellite-stable (MSS) colorectal cancer (CRC) is less well-studied. Here, using single-cell RNA-sequencing, the authors identify a Vδ1 + T cell subset, which are functionally impaired in MSS CRC via a TIGIT-NECTIN2 interaction.

Metabolic control is mediated by the dynamic assemblies and function of multiple redox enzymes. A key element in these assemblies, the P450 oxidoreductase (POR), donates electrons and selectively activates numerous (>50 in humans and >300 in plants) cytochromes P450 (CYPs) controlling metabolism of drugs, steroids and xenobiotics in humans and natural product biosynthesis in plants. The mechanisms underlying POR-mediated CYP metabolism remain poorly understood and to date no ligand binding has been described to regulate the specificity of POR. Here, using a combination of computational modeling and functional assays, we identify ligands that dock on POR and bias its specificity towards CYP redox partners, across mammal and plant kingdom. Single molecule FRET studies reveal ligand binding to alter POR conformational sampling, which results in biased activation of metabolic cascades in whole cell assays. We propose the model of biased metabolism, a mechanism akin to biased signaling of GPCRs, where ligand binding on POR stabilizes different conformational states that are linked to distinct metabolic outcomes. Biased metabolism may allow designing pathway-specific therapeutics or personalized food suppressing undesired, disease-related, metabolic pathways. P450 oxidoreductase (POR) selectively activates numerous cytochromes P450 (CYP), crucial for metabolism of drugs, steroids and xenobiotics and natural product biosynthesis. Here, the authors identify ligands that bind POR and bias its specificity towards CYP redox partners, activating distinct metabolic cascades in cells.

The current study highlights the use of binary logistic regression for land-use land-cover (LULC) classification. The moderate-resolution Sentinel-2 multispectral data was used for LULC map generation for the post-monsoon season. The main focus of this study is to present a simple and precise approach for image classification using binary logistic regression (BLR) technique. The study was carried out in cropland, fallow land, forest and water body dominated subtropical region of India located in the eastern coastal region. The cropland and fallow lands are mostly dependent on the monsoon and reciprocal land covers. A large number of training and testing data points were collected viewing the image in a standard false-color composite. ArcGIS, Microsoft Office Excel and R software were used for classification. In addition to BLR, the training and testing data points were also used to perform the classification with ‘random forest’ classifier in R. We observed higher classification accuracy for spectrally pure classes and pixels and lower for closely associated mix-pixels. Lower user’s and producer’s accuracies (< 90%) were observed for fallow land, water body and grassland class during training and model building and for fallow land and forest during accuracy assessment, whereas the accuracies were more than 90% for the rest of classes during both training and testing. Misclassifications were mostly observed between forest, fallow land, grassland and water body during training, which were forest and fallow land in testing, due to their lower spectral difference with reference to classified classes. However, the overall accuracy and kappa value during training and testing were more than 94% and 0.98, respectively. Similar accuracies and misclassification were also obtained with the results of random forest model, validating the adopted methodology. Regardless of the seasonal variations in cropland and fallow land, the field observations (52 locations) also corroborated the estimated classification accuracy. The easy implementation and comparatively higher classification accuracy with the binary logistic technique are believed to increase its intense use in land-cover classification.