Estimation of the lateral variation of photon beam energy spectra using the percentage depth dose reconstruction method.

In photon-collapsed cone convolution (pCCC) algorithm of the Monaco treatment planning system (TPS), the central-axis energy spectrum is assumed constant throughout the entire irradiation area. To consider lateral variations, an off-axis softening factor is applied to attenuation coefficients during the total energy released per unit mass calculation. We evaluated this method through comparison studies of percentage depth doses (PDDs) and off-axis ratios (OARs) calculated by Monaco and measured for a 6 MV photon beam at various off-axis angles and depths. Significant differences were observed, with relative differences exceeding ± 1%. Therefore, this method may not accurately represent lateral variations of energy spectra. We propose directly implementing energy spectra on both central-axis and off-axis to improve dose calculation accuracy for large field. To this end, we introduce reconstruction of PDDs from monoenergetic depth doses (MDDs) along off-axis angles, thereby estimating energy spectra as functions of radial distance. This method derives energy spectra quickly without significantly increasing the beam modeling time. MDDs were computed through Monte Carlo simulations (DOSRZnrc). The variances between reconstructed and measured PDDs were minimized using the generalized-reduced-gradient method to optimize energy spectra. Reconstructed PDDs along off-axis angles of 0°, 1.15°, 2.29°, 3.43°, 4.57°, 5.71°, 6.84°, 7.97°, 9.09°, 10.2° to estimate energy spectra at radial distances of 0-18 cm in 2 cm increments and OARs calculated using estimated energy spectra at 5, 10, and 20 cm depths, well agreed with measurement (relative differences within ± 0.5%). In conclusion, our proposed method accurately estimates lateral energy spectrum variation, thereby improving dose calculation accuracy of pCCC algorithm.

Low pH modulates lipopolysaccharide-induced tumor necrosis factor-alpha expression and macropinocytotic activity in RAW264.7 cells.

Inflammation triggers various types of diseases that need to be addressed. Macrophages play important roles in the inflammatory responses. As atherosclerosis progresses, macrophages transform into foam cells. Extracellular acidification is observed at and around bacterial infection and atherosclerotic sites. However, the effects of acidification on the inflammatory response of macrophages and the progression of atherosclerosis have not been fully understood. This study investigates the impact of extracellular acidification on lipopolysaccharide-induced tumor necrosis factor-alpha (TNF-α) expression and macropinocytotic activity in RAW264.7 cells. TNF-α expression is measured by real-time polymerase chain reaction (relative value to glyceraldehyde-3-phosphate dehydrogenase expression). Macropinocytotic activity is measured by neutral red uptake (absorbance at 540 nm). Results show that TNF-α expression increased with decreasing extracellular pH in both un-foamed and foamed cells. Macropinocytotic activity was upregulated at pH 6.8 in un-foamed cells, but downregulated in foamed cells stimulated at low pH. Proton-sensing G protein-coupled receptors (GPCRs) were involved in the expression of TNF-α and in the macropinocytotic activity of foamed cells. In conclusion, this study reveals that extracellular acidification differently affect various inflammatory responses such as LPS-induced TNF-α expression and macropinocytotic activity of RAW264.7 cells and different proton-sensing GPCRs are involved in the different inflammatory responses.

Machine learning of atomic force microscopy images of organic solar cells.

The bulk heterojunction structures of organic photovoltaics (OPVs) have been overlooked in their machine learning (ML) approach despite their presumably significant impact on power conversion efficiency (PCE). In this study, we examined the use of atomic force microscopy (AFM) images to construct an ML model for predicting the PCE of polymer : non-fullerene molecular acceptor OPVs. We manually collected experimentally observed AFM images from the literature, applied data curing and performed image analyses (fast Fourier transform, FFT; gray-level co-occurrence matrix, GLCM; histogram analysis, HA) and ML linear regression. The accuracy of the model did not considerably improve even by including AFM data in addition to the chemical structure fingerprints, material properties and process parameters. However, we found that a specific spatial wavelength of FFT (40-65 nm) significantly affects PCE. The GLCM and HA methods, such as homogeneity, correlation and skewness expand the scope of image analysis and artificial intelligence in materials science research fields.

Biologically Equivalent Dose Comparison Between Magnetic Resonance-Guided Adaptive and Computed Tomography-Guided Internal Target Volume-Based Stereotactic Body Radiotherapy for Liver Tumors.

Background and aim Magnetic resonance (MR) imaging has been increasingly adopted in the field of radiotherapy, and the most advanced MR image-guided radiotherapy is known as MR-guided online adaptive radiotherapy (MRgOART), which integrates MRI and linac systems. Few attempts have yet been made to directly compare treatment outcomes between the MRgOART and standard computed tomography (CT)-guided radiotherapy (CTgRT). Besides, it is reported that the biologically equivalent dose (BED) may be a good predictor of the local control (LC) and the overall survival (OS) for liver tumors. The purpose of this study is to compare the BEDs between the MRgOART and the CTgRT by way of virtual isotoxic planning for liver tumors. The hypothesis of this study is therefore that the MRgOART increases LC and OS as compared to the CTgRT. Materials and methods Using the five patient cases available, isotoxic planning was performed. For CTgRT, an internal target volume (ITV) was defined, and the planning target volume (PTV) was created by adding an isotropic margin of 10 mm. For MRgRT, a gross tumor volume (GTV) was defined, and the PTV was created by adding an isotropic margin of 5 mm. Each tumor size was virtually adjusted so that the CTgRT plans resulted in BED <100 Gy under the condition that the nearest organs at risk receive maximum tolerated doses. Subsequently, the BED was recalculated for MRgOART plans with the adjusted tumor size. Results and discussion It was found that the BEDs of the MRgOART plans always exceeded 100 Gy and were approximately 20 Gy larger than those of the corresponding CTgRT plans. Literature shows that superior overall survival rates for liver tumors were observed when BED was >100 Gy as compared to BED <100 Gy, suggesting that MR-guided adaptive planning may potentially lead to better treatment outcomes for liver tumors. We have also observed a case where the duodenum largely moved and abutted the liver after the CT images were acquired, indicating a significant disadvantage of the standard CTgRT because such abutting is not observable by the cone-beam CT immediately before treatment. Conclusion A highly accelerated evidence-creation procedure to suggest the clinical superiority of MRgOART has been arguably proposed with promising results. The sample size is small and limits the extent to which the findings in this study can be generalized. Further virtual clinical trials within the radiotherapy community are awaited with more clinical outcomes data.

Ethylenediaminetetraacetic acid (EDTA) enhances cAMP production in human TDAG8-expressing cells.

Ethylenediaminetetraacetic acid (EDTA) is a chelating agent that binds tightly to metal ions. We found that cAMP response element (CRE)-driven promoter activity by protons was enhanced by EDTA in human T-cell death-associated gene 8 (TDAG8)-overexpressed HEK293T cells. The enhancing action by EDTA was also detected by proton-induced cAMP production that is located upstream from the CRE-driven promoter activity even at physiological proton concentration pH7.4. The proton-induced CRE-driven promoter activity was not enhanced by other chelating agents, ethylene glycol tetraacetic acid (EGTA) and sodium citrate. The enhanced CRE-driven promoter activity by EDTA was not attenuated by increasing the extracellular calcium ion concentration. These results indicate that the EDTA-enhancing action may not be due to its chelating action but might rather be another EDTA-specific effect. Enhanced cAMP production by EDTA was also detected in a human leukemia cell line HL-60, in which TDAG8 and OGR1 (ovarian cancer G-protein-coupled receptor 1) were endogenously expressed, suggesting that the medical use of EDTA would influence the physiological and pathophysiological functions of hematopoietic cells.

Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks.

Nonfullerene, a small molecular electron acceptor, has substantially improved the power conversion efficiency of organic photovoltaics (OPVs). However, the large structural freedom of π-conjugated polymers and molecules makes it difficult to explore with limited resources. Machine learning, which is based on rapidly growing artificial intelligence technology, is a high-throughput method to accelerate the speed of material design and process optimization; however, it suffers from limitations in terms of prediction accuracy, interpretability, data collection, and available data (particularly, experimental data). This recognition motivates the present Perspective, which focuses on utilizing the experimental data set for ML to efficiently aid OPV research. This Perspective discusses the trends in ML-OPV publications, the NFA category, and the effects of data size and explanatory variables (fingerprints or Mordred descriptors) on the prediction accuracy and explainability, which broadens the scope of ML and would be useful for the development of next-generation solar cell materials.