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Background: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision‐making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. Purpose: To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). Study Type: Retrospective. Population: Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). Field Strength/Sequences: T2‐weighted imaging with a turbo spin echo sequence and diffusion‐weighted imaging with a single‐shot echo plane‐imaging sequence at 3.0 T. Assessment: The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin‐Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate‐specific antigen, ADC value, and the lesion maximum cross‐sectional diameter. Statistical Tests: Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. Results: The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726–0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702–0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656–0.827]) and RI (0.664 [95% CI, 0.566–0.752]). Data Conclusion: TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. Level of Evidence: 4 Technical Efficacy: Stage 4 [ABSTRACT FROM AUTHOR]

Yin chai hu (Radix Stellariae) is a root medicine that is frequently used in Chinese traditional medicine to treat fever and malnutrition. In modern medicine, it has been discovered to have anti‐inflammatory, anti‐allergic, and anticancer properties. In a previous study, we were able to extract lipids from Stellariae Radix using supercritical CO2 extraction (SRE), and these sterol lipids accounted for up to 88.29% of the extract. However, the impact of SRE on the development of atopic dermatitis (AD) has not yet been investigated. This study investigates the inhibitory effects of SRE on AD development using a 2,4‐dinitrochlorobenzene (DNCB)‐induced AD mouse model. Treatment with SRE significantly reduced the dermatitis score and histopathological changes compared with the DNCB group. The study found that treatment with SRE resulted in a decrease of pro‐inflammatory cytokines TNF‐α, CXC‐10, IL‐12, and IL‐1β in skin lesions. Additionally, immunohistochemical analysis revealed that SRE effectively suppressed M1 macrophage infiltration into the AD lesion. Furthermore, the anti‐inflammatory effect of SRE was evaluated in LPS + INF‐γ induced bone marrow‐derived macrophages (BMDMs) M1 polarization, SRE inhibited the production of TNF‐α, CXC‐10, IL‐12, and IL‐1β and decreased the expression of NLRP3. Additionally, SRE was found to increase p‐AMPKT172, but had no effect on total AMPK expression, after administration of the AMPK inhibitor Compound C, the inhibitory effect of SRE on M1 macrophages was partially reversed. The results indicate that SRE has an inhibitory effect on AD, making it a potential therapeutic agent for this atopic disorder. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) transition metal nitrides and carbides (MXenes), represented by Ti3C2Tx, have broad applications in flexible electronics, electromechanical devices, and structural membranes due to their unique physical and chemical properties. Despite the Young's modulus of 2D Ti3C2Tx has been theoretically predicted to be 0.502 TPa, which has not been experimentally confirmed so far due to the measurement is extremely restricted. Here, by optimizing the sample preparation, cutting, and transfer protocols, we perform the direct in-situ tensile tests on monolayer Ti3C2Tx nanosheets using nanomechanical push-to-pull equipment under a scanning electron microscope. The effective Young's modulus is 0.484 ± 0.013 TPa, which is much closer to the theoretical value of 0.502 TPa than the previously reported 0.33 TPa by the disputed nanoindentation method, and the measured elastic stiffness is ~948 N/m. Moreover, during the process of tensile loading, the monolayer Ti3C2Tx shows an average elastic strain of ~3.2% and a tensile strength as large as ~15.4 GPa. This work corrects the previous reports by nanoindentation method and demonstrates that the Ti3C2Tx indeed keeps immense potential for broad range of applications. The Young's modulus of the MXene Ti3C2Tx, theoretically predicted to be 0.502 TPa, has not yet been experimentally confirmed. This work supplants previous reports using nanoindentation with the correctly measured Young's modulus of 0.484 ± 0.013 TPa. [ABSTRACT FROM AUTHOR]

The original plant of Chinese medicine Stellariae Radix (Yin Chai Hu) is Stellaria dichotoma L. var. lanceolata Bge (abbreviated as SDL). SDL is a perennial herbaceous plant and a characteristic crop in Ningxia. Growth years are vital factors that affect the quality of perennial medicinal materials. This study aims to investigate the impact of growth years on SDL and screen for the optimal harvest age by comparing the medicinal material characteristics of SDL with different growth years. Additionally, metabolomics analysis using UHPLC-Q-TOF MS was employed to investigate the impact of growth years on the accumulation of metabolites in SDL. The results show that the characteristics of medicinal materials and the drying rate of SDL gradually increase with the increase in growth years. The fastest development period of SDL occurred during the first 3 years, after which the development slowed down. Medicinal materials characteristics of 3-year-old SDL exhibited mature qualities with a high drying rate, methanol extract content, and the highest content of total sterols and total flavonoids. A total of 1586 metabolites were identified, which were classified into 13 major classes with more than 50 sub-classes. Multivariate statistical analysis indicated significant differences in the diversity of metabolites of SDL in different growth years, with greater differences observed in metabolites as the growth years increased. Moreover, different highly expressed metabolites in SDL at different growth years were observed: 1–2 years old was beneficial to the accumulation of more lipids, while 3–5 years old was conducive to accumulating more alkaloids, benzenoids, etc. Furthermore, 12 metabolites accumulating with growth years and 20 metabolites decreasing with growth years were screened, and 17 significantly different metabolites were noted in 3-year-old SDL. In conclusion, growth years not only influenced medicinal material characteristics, drying rate, content of methanol extract, and total sterol and flavonoid contents, but also had a considerable effect on SDL metabolites and metabolic pathways. SDL planted for 3 years presented the optimum harvest time. The screened significantly different metabolites with biological activity, such as rutin, cucurbitacin e, isorhamnetin-3-o-glucoside, etc., can be utilized as potential quality markers of SDL. This research provides references for studying the growth and development of SDL medicinal materials, the accumulation of metabolites, and the selection of optimal harvest time. [ABSTRACT FROM AUTHOR]