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This study addresses the heterogeneity of breast cancer (BC) by employing a Conditional Probabilistic Diffusion Model (CPDM) to synthesize Magnetic Resonance Images (MRIs) based on multi-omic data, including gene expression, copy number variation, and DNA methylation. The lack of paired medical images and genomics data in previous studies presented a challenge, which the CPDM aims to overcome. The well-trained CPDM successfully generated synthetic MRIs for 726 TCGA-BRCA patients, who lacked actual MRIs, using their multi-omic profiles. Evaluation metrics such as Frechet's Inception Distance (FID), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) demonstrated the CPDM's effectiveness, with an FID of 2.02, MSE of 0.02, and SSIM of 0.59 based on the 15-fold cross-validation. The synthetic MRIs were used to predict clinical attributes, achieving an Area Under the Receiver-Operating-Characteristic curve (AUROC) of 0.82 and an Area Under the Precision-Recall Curve (AUPRC) of 0.84 for predicting ER+/HER2+ subtypes. Additionally, the MRIs accurately predicted BC patient survival with a Concordance-index (C-index) score of 0.88, outperforming other baseline models. This research demonstrates the potential of CPDMs in generating MRIs based on BC patients' genomic profiles, offering valuable insights for radiogenomic research and advancements in precision medicine. The study provides a novel approach to understanding BC heterogeneity for early detection and personalized treatment. Author summary: Breast cancer (BC) is known for its diverse characteristics, which makes it crucial for early detection and personalized treatment. Combining medical images with genomics provides a fresh approach to studying this diversity, leading to the emergence of a new field called radiogenomics. However, when these two data types (image data and genomic data) are not paired, it becomes a challenge. This study proposes the use of a well-trained Conditional Probabilistic Diffusion Model (CPDM) to address this issue by generating BC medical images based on genomic information. CPDM is a type of advanced Artificial Intelligence (AI)-based generative model, like ChatGPT. CPDM has been very successful in creating images that look real. In this study, we built and trained a CPDM specifically for BC. The well-trained CPDM can generate BC medical images very well and the generated images can accurately predict patients' clinical attributes like gene mutations, receptor statuses, and survival probabilities. This research explores the potential of using CPDMs to generate meaningful medical images from genomic data, aiding in solving crucial clinical problems. These findings have implications for advancing radiogenomic research and the development of personalized medicine approaches using AI. [ABSTRACT FROM AUTHOR]

Background: Rosacea is difficult to cure and frequently recurs. Topical photodynamic therapy (PDT) has been tentatively used, with only preliminary results reported. Objective: To evaluate the efficacy and safety of topical PDT in Chinese patients with rosacea. Methods & Materials: Seventeen participants with rosacea were treated three times using 5-aminolevulinic acid (ALA)-PDT at intervals of 7–10 days. Papule and pustule numbers, erythema severity, telangiectasia severity, physician's global assessment (PGA) score (1 [best]–6), and patient satisfaction score (0–3 [highest]) were assessed. Rosacea improvement and the total effective rate were calculated. Stratum corneum hydration and sebum levels, and the melanin index (MI) and erythema index (EI) were measured non-invasively. Results: After three treatments with ALA-PDT, the total effective rate (≥50% improvement) was 64.71%, mean PGA score was 2.88 ± 0.93, and mean patient satisfaction score was 1.71 ± 0.69. The EI significantly decreased 1 month after the final treatment (from 468 ± 80.61 to 439 ± 77.78 for the forehead and from 507.65 ± 92.51 to 483.27 ± 78.32 for the nasal ala). Four participants received three additional treatments. They achieved 50–74% improvement after three treatments and ≥75% improvement after six treatments. Conclusion: ALA-PDT is safe and effective for treating rosacea. [ABSTRACT FROM AUTHOR]

The instability of nanoemulsions were mainly due to Ostwald ripening. The droplet charge was influenced by the stability of nanoemulsions significantly. In this work, the properties of the shea butter oil-loaded nanoemulsions were investigated in detail with the addition of cationic surfactants (cetyl trimethyl ammonium chloride, 1631; octadecyl trimethyl ammonium chloride, 1831), anionic surfactants (alcohol ethoxysulfate, AES; dodecyl phosphate ester sodium salt, MAP), and zwitterionic surfactants (cocoamidopropyl betaine, CAB; dodecyl hydroxysulfobetaine, 20HD). By increasing the concentration of cationic surfactants, the positively charged nanoemulsions were prepared and the smallest droplets were being formed with 0.05% 1831. Upon the addition of anionic surfactants, a more negative value was obtained and the smallest droplets were being formed with 0.1% AES. The ionic surfactants by increasing the electrostatic interactions between droplets and incorporation into the oil phase improved the stability of the nanoemulsions via lowering the Ostwald ripening rate, and especially improved the high temperature stability. By increasing the concentration of zwitterionic surfactants, a less negative zeta potential was observed and the stability of the nanoemulsions did not improve. The results proved that the electrosteric repulsion had an appreciable impact on the stability of the nanoemulsions. [ABSTRACT FROM PUBLISHER]

Factors that affect the phase-inversion temperature (PIT) based on nonionic surfactant fatty alcohol ethoxylates were investigated. Phase-inversion process was continuously monitored by determining changes in conductivity with temperature. The influences of oil-to-water ratio, emulsifier concentration, emulsifier mixing ratio, sodium chloride, and oil types on the PIT of emulsions were investigated. Results showed that the PIT of the emulsions declined with increased oil-to-water ratio. Emulsifier concentration significantly affected the PIT temperature. High sodium chloride content suppressed phase inversion. A lower Brij72-to-Brij721 ratio corresponded to higher PIT. Different oils required different HLB numbers in the phase-inversion process. [ABSTRACT FROM PUBLISHER]

Shea butter nanoemulsions were optimized using response surface design methods. Pseudo-ternary phase diagrams were employed to find the favorable region for preparing the coarse emulsion, which was subsequently used to prepare the nanoemulsions. With the addition of 0.2 wt% NaCl, the coarse emulsion region increased. We considered the influence of homogenization pressure and number of passes on the particle size, and there was a linear decrease in the logarithm of the mean droplet diameter with the logarithm of the homogenization pressure. According to the response surface design analysis (RSD), NaCl (0.30 wt%) can significantly reduce the particle size of nanoemulsions. Simultaneously, it can also reduce the polydispersity index (PDI). The oil concentration also has significant impact on the particle size; however, it has faint influence on the PDI. Optimum formulation and conditions for minimum particle size and the highest stability were found at 5.0 wt% mixed emulsifiers, 10.0 wt% oil content, 0.3 wt% NaCl content, 8 pass times of homogenization and 100 MPa homogenization pressure. [ABSTRACT FROM PUBLISHER]