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Introduction: Despite the available secondary preventive treatments, the management of stable coronary artery disease (SCAD) remains challenging. Intermediate coronary lesion (ICL), defined as luminal stenosis between 50% and 70%, is a key stage of SCAD. However, existing therapeutic strategies are limitated in delaying plaque progression and associated with various adverse effects and economic burdens. Qing-Xin-Jie-Yu Granules (QXJYG) with proven anti-platelet, anti-inflammatory, and lipid-lowering effects may compensate for the drawbacks of current treatments and can be tested as a complementary therapy. Therefore, this study aims to investigate the efficacy and safety of QXJYG in treating ICL, with a particular focus on its impact on myocardial ischemia and plaque progression. Materials and methods: This is a multicenter, randomized, double-blind, placebo-controlled trial. A total of 120 participants with ICL will be randomly assigned to two groups in a 1:1 ratio. In addition to basic medications, the intervention group will receive QXJYG, while the control group will receive a placebo for over 6 months, followed by a 12-month follow-up. The primary efficacy outcome is computed tomography-derived fractional flow reserve. The secondary outcomes include the degree of coronary stenosis, coronary artery calcification score, Gensini score, Seattle Angina Questionnaire score, high-sensitivity C-reactive protein, matrix metalloproteinase-9, blood lipids, and carotid artery ultrasound parameters. Major adverse cardiovascular events are recorded as endpoints. The safety outcomes include composite events of bleeding, laboratory test results, and adverse events. Clinical visits are scheduled at baseline, every 2 months during the treatment, and after a 12-month follow-up. Discussion: This trial is anticipated to yield reliable results to verify the efficacy and safety of QXJYG in the treatment of ICL, which will provide novel insights to help address the prevailing therapeutic dilemma of ICL, thereby facilitating for the management of SCAD. Trial registration: Chinese Clinical Trial Registry, ChiCTR2200059262. Registered on April 27, 2022. [ABSTRACT FROM AUTHOR]

Background: Endosonographers are highly dependent on the diagnosis of pancreatic ductal adenocarcinoma (PDAC). The objectives of this study were to develop a deep‐learning radiomics (DLR) model based on endoscopic ultrasonography (EUS) images for identifying PDAC and to explore its true clinical benefit. Methods: A retrospective data set of EUS images that included PDAC and benign lesions was used as a training cohort (N = 368 patients) to develop the DLR model, and a prospective data set was used as a test cohort (N = 123 patients) to validate the effectiveness of the DLR model. In addition, seven endosonographers performed two rounds of reader studies on the test cohort with or without DLR assistance to further assess the clinical applicability and true benefits of the DLR model. Results: In the prospective test cohort, DLR exhibited an area under the receiver operating characteristic curves of 0.936 (95% confidence interval [CI], 0.889–0.976) with a sensitivity of 0.831 (95% CI, 0.746–0.913) and 0.904 (95% CI, 0.820–0.980), respectively. With DLR assistance, the overall diagnostic performance of the seven endosonographers improved: one endosonographer achieved a significant expansion of specificity (p =.035,) and another achieved a significant increase in sensitivity (p =.038). In the junior endosonographer group, the diagnostic performance with the help of the DLR was higher than or comparable to that of the senior endosonographer group without DLR assistance. Conclusions: A prospective test cohort validated that the DLR model based on EUS images effectively identified PDAC. With the assistance of this model, the gap between endosonographers at different levels of experience narrowed, and the accuracy of endosonographers expanded. The deep‐learning radiomics model improved the diagnostic accuracy, sensitivity, and specificity of all endosonographers in the test cohort. With the aid of our model, the decision‐making accuracy of all endosonographers was further improved. The deep learning radiomics model is a tool with clinical application potential that can assist endosonographers in pancreatic ductal adenocarcinoma diagnosis. [ABSTRACT FROM AUTHOR]

The Chinese white pine beetle (Dendroctonus armandi Tsai and Li) is the causal organism for the colossal death of the Chinese white pine (Pinus armandi Fr). We investigated the change of monoterpenes in the stem pholem and xylem in P. armandi saplings at 4 days, 8 days, and 30 days after treatment using treatment (MeJa + Tween-20 and Tween-20 as control) and untreated. GC-MS analysis found that the total monoterpene concentrations in the pholem and xylem in MeJa-treated saplings increased significantly compared with control or untreated saplings. Additionally, the proportions of β-pinene in the stem pholem, β-myrcene in the stem xylem, and limonene + β-phellandrene in both pholem and xylem of MeJa-treated saplings were increased significantly compared with control or untreated saplings. Based on our results, we suggest that by increasing the total concentration of monoterpene and the concentrations of β-myrcene, β-pinene and limonene + β-phellandrene maybe play an important defensive role against insect pests in P. armandi defense.