Regulation of the P53 tumor suppressor gene and the Mcl-2 oncogene expression by an active herbal component delivered through a smart thermo-pH-sensitive PLGA carrier to improve Osteosarcoma treatment.

Osteosarcoma (OS), a lethal malignancy, has witnessed an escalating incidence rate. Contemporary therapeutic strategies for this cancer have proven to be inadequate, primarily due to their extensive side effects and the lack of specificity in targeting the molecular pathways implicated in this disease. Consequently, this project is aimed to manufacture and characterize Poly (Lactic-co-glycolic acid) embodying curcumin, a phytocompound devoid of adverse effects which not only exerts an anti-neoplastic influence but also significantly modulates the genetic pathways associated with this malignancy. In this investigation, multiple formulations of PLGA-Cur were synthesized, and the choice of optimal formula was made considering the efficiency of nanoparticle encapsulation and the drug dispersion rate from synthesized PLGA. The selected formulation's physical and chemical attributes, such as its dimension, polydispersity index of the formulation, surface electrical charge, physical-spatial structure, and stability, were examined using methods, including Dynamic light scattering (DLS), Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), and spectrophotometry. Subsequently, the absence of interaction between the drug and the system was assessed using Fourier Transform Infrared Spectroscopy (FT-IR), and cellular uptake was evaluated using fluorescence microscopy. The smart system's responsiveness to environmental stimuli was determined using the dialysis bag method and its anti-tumor properties were investigated on the SAOS-2 cell line. Finally, to evaluate the system's genetic impact on bone cancer, the molecular quantification of the P53 tumor suppressor gene and the oncogene MCL-2 was analyzed using real-time PCR and their protein expression levels were also examined. The PLGAs synthesized in this study exhibited an encapsulation rate of 91.5 ± 1.16% and a maximum release rate of 71 ± 1%, which were responsive to various stimuli. The size of the PLGAs was 12.5 ± 321.2 nm, with an electric charge of -38.9 ± 2.6 mV and a PDI of 0.107, indicating suitable morphology and stability. Furthermore, both the system and the drug retained their natural properties after inoculation. The system was readily absorbed by cancer cells and effectively exerted its anti-cancer properties. Notably, the system had a significant impact on the mentioned genes' expression. The produced nanosystem, possessing optimal physicochemical properties, has the potential to enhance the anti-cancer efficacy of curcumin. This is achieved by altering molecular and genetic pathways within cancer cells, thereby positioning it as a viable adjunctive treatment modality and also synthesizing of this herbal base drug system consider as a completely novel method for cancer therapy that can efficiently modulate genetical pathways involved.

Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state.

The sliding window correlation (SWC) analysis is a straightforward and common approach for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses have been long used, there are still considerable technical issues associated with the approach. A great effort has recently been dedicated to investigate the window setting effects on dynamic connectivity estimation. In this direction, tapered windows have been proposed to alleviate the effect of sudden changes associated with the edges of rectangular windows. Nevertheless, the majority of the windows exploited to estimate brain connectivity tend to suppress dynamic correlations, especially those with faster variations over time. Here, we introduced a window named modulated rectangular (mRect) to address the suppressing effect associated with the conventional windows. We provided a frequency domain analysis using simulated time series to investigate how sliding window analysis (using the regular window functions, e.g. rectangular and tapered windows) may lead to unwanted spectral modulations, and then we showed how this issue can be alleviated through the mRect window. Moreover, we created simulated dynamic network data with altering states over time using simulated fMRI time series, to examine the performance of different windows in tracking network states. We quantified the state identification rate of different window functions through the Jaccard index, and observed superior performance of the mRect window compared to the conventional window functions. Overall, the proposed window function provides an approach that improves SWC estimations, and thus the subsequent inferences and interpretations based on the connectivity network analyses.

Single-shot coherent noise suppression by spatial interferometric heterodyning.

Coherent noise affects the information content in active imaging systems. Here we show that noise reduction can be accomplished using the intrinsic coherence properties of the electromagnetic fields. We demonstrate numerically and experimentally that a single-shot measurement using interferometric spatial heterodyning detection in conjuncture with computational image processing, permits suppressing coherent noise in conditions of low signal-to-noise ratios, through spectral upshifting of coherent information.

Compressive correlation imaging with random illumination.

Achieving high rates and high resolution in noisy conditions is a main desiderate for computational imaging. Here, we demonstrate both numerically and experimentally that binary illumination based on spatially random distributions provides superior imaging capabilities at high compression ratios while operating in noisy environments. The proposed method permits decreasing the time for image formation without adding complexity to the imaging system or compromising the resolution.