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Numerical modeling of the rapid transition at a hydraulic jump is challenging considering the complex coupling between air transportation and turbulence evolution at various length scales. In this paper, the turbulent air–water flow properties in A-type hydraulic jumps over a negative step were investigated and compared to those in classic hydraulic jump numerically. The model solves the Reynolds-averaged Navier–Stokes (RANS) equations for mean flow motion, the modified k–ε model for turbulence closure, and an advanced transient model for void fraction distribution. The numerical model was validated by systematically comparing the simulated free-surface profile, time-averaged velocity and void fraction distributions against experimental results developed in parallel to the numerical modeling. The validated model reproduced successfully the basic air-water flow properties, including the formation of a bottom roller in the step cavity below the A-type jumps. The simulation revealed further turbulence characteristics of the aerated flow and their evolution over the abrupt drop. The stepped bottom introduced secondary vorticity in the lower shear layer that enhanced energy dissipation and reduced the bottom boundary shear force, thus providing bed surface protection. It was also able to help with stabilization of the hydraulic jump position by weakening the jump toe oscillation. [ABSTRACT FROM AUTHOR]

(1) Background: Radiation-induced thrombocytopenia (RIT) often occurs in cancer patients undergoing radiation therapy, which can result in morbidity and even death. However, a notable deficiency exists in the availability of specific drugs designed for the treatment of RIT. (2) Methods: In our pursuit of new drugs for RIT treatment, we employed three deep learning (DL) algorithms: convolutional neural network (CNN), deep neural network (DNN), and a hybrid neural network that combines the computational characteristics of the two. These algorithms construct computational models that can screen compounds for drug activity by utilizing the distinct physicochemical properties of the molecules. The best model underwent testing using a set of 10 drugs endorsed by the US Food and Drug Administration (FDA) specifically for the treatment of thrombocytopenia. (3) Results: The Hybrid CNN+DNN (HCD) model demonstrated the most effective predictive performance on the test dataset, achieving an accuracy of 98.3% and a precision of 97.0%. Both metrics surpassed the performance of the other models, and the model predicted that seven FDA drugs would exhibit activity. Isochlorogenic acid A, identified through screening the Chinese Pharmacopoeia Natural Product Library, was subsequently subjected to experimental verification. The results indicated a substantial enhancement in the differentiation and maturation of megakaryocytes (MKs), along with a notable increase in platelet production. (4) Conclusions: This underscores the potential therapeutic efficacy of isochlorogenic acid A in addressing RIT. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks." The correction addresses an error in Figure 5 of the original publication, where the ICGA-A (10 μM) group of K562 cells was incorrectly placed. The authors state that this error does not affect the scientific conclusions of the study. The corrected figure is provided in the document. [Extracted from the article]

Background: Thrombocytopenia has long been considered an important complication of chemotherapy and radiotherapy, which severely limits the effectiveness of cancer treatment and the overall survival of patients. However, clinical treatment options are extremely limited so far. Ruxolitinib is a potential candidate. Methods: The impact of ruxolitinib on the differentiation and maturation of K562 and Meg-01 cells megakaryocytes (MKs) was examined by flow cytometry, Giemsa and Phalloidin staining. A mouse model of radiation-injured thrombocytopenia (RIT) was employed to evaluate the action of ruxolitinib on thrombocytopoiesis. Network pharmacology, molecular docking, drug affinity responsive target stability assay (DARTS), RNA sequencing, protein blotting and immunofluorescence analysis were applied to explore the targets and mechanisms of action of ruxolitinib. Results: Ruxolitinib can stimulate MK differentiation and maturation in a dose-dependent manner and accelerates recovery of MKs and thrombocytopoiesis in RIT mice. Biological targeting analysis showed that ruxolitinib binds directly to Toll Like Receptor 2 (TLR2) to activate Rac1/cdc42/JNK, and this action was shown to be blocked by C29, a specific inhibitor of TLR2. Conclusions: Ruxolitinib was first identified to facilitate MK differentiation and thrombocytopoiesis, which may alleviate RIT. The potential mechanism of ruxolitinib was to promote MK differentiation via activating the Rac1/cdc42/JNK pathway through binding to TLR2. [ABSTRACT FROM AUTHOR]