Neutrophil S100A9 supports M2 macrophage niche formation in granulomas

慢性炎症「肉芽腫」における好中球の新しい炎症制御系の解明 --M2マクロファージの新たな誘導メカニズム解明--. 京都大学プレスリリース. 2023-02-17.In search of inflammatory Achilles heel. 京都大学プレスリリース. 2023-03-10.Mycobacterium infection gives rise to granulomas predominantly composed of inflammatory M1-like macrophages, with bacteria-permissive M2 macrophages also detected in deep granulomas. Our histological analysis of Mycobacterium bovis bacillus Calmette-Guerin-elicited granulomas in guinea pigs revealed that S100A9-expressing neutrophils bordered a unique M2 niche within the inner circle of concentrically multilayered granulomas. We evaluated the effect of S100A9 on macrophage M2 polarization based on guinea pig studies. S100A9-deficient mouse neutrophils abrogated M2 polarization, which was critically dependent on COX-2 signaling in neutrophils. Mechanistic evidence suggested that nuclear S100A9 interacts with C/EBPβ, which cooperatively activates the Cox-2 promoter and amplifies prostaglandin E2 production, followed by M2 polarization in proximal macrophages. Because the M2 populations in guinea pig granulomas were abolished via treatment with celecoxib, a selective COX-2 inhibitor, we propose the S100A9/Cox-2 axis as a major pathway driving M2 niche formation in granulomas

Present developments in reaching an international consensus for a model-based approach to particle beam therapy

Particle beam therapy (PBT), including proton and carbon ion therapy, is an emerging innovative treatment for cancer patients. Due to the high cost of and limited access to treatment, meticulous selection of patients who would benefit most from PBT, when compared with standard X-ray therapy (XRT), is necessary. Due to the cost and labor involved in randomized controlled trials, the model-based approach (MBA) is used as an alternative means of establishing scientific evidence in medicine, and it can be improved continuously. Good databases and reasonable models are crucial for the reliability of this approach. The tumor control probability and normal tissue complication probability models are good illustrations of the advantages of PBT, but pre-existing NTCP models have been derived from historical patient treatments from the XRT era. This highlights the necessity of prospectively analyzing specific treatment-related toxicities in order to develop PBT-compatible models. An international consensus has been reached at the Global Institution for Collaborative Research and Education (GI-CoRE) joint symposium, concluding that a systematically developed model is required for model accuracy and performance. Six important steps that need to be observed in these considerations include patient selection, treatment planning, beam delivery, dose verification, response assessment, and data analysis. Advanced technologies in radiotherapy and computer science can be integrated to improve the efficacy of a treatment. Model validation and appropriately defined thresholds in a cost-effectiveness centered manner, together with quality assurance in the treatment planning, have to be achieved prior to clinical implementation

Structural insight into the TFIIE–TFIIH interaction: TFIIE and p53 share the binding region on TFIIH

RNA polymerase II and general transcription factors (GTFs) assemble on a promoter to form a transcription preinitiation complex (PIC). Among the GTFs, TFIIE recruits TFIIH to complete the PIC formation and regulates enzymatic activities of TFIIH. However, the mode of binding between TFIIE and TFIIH is poorly understood. Here, we demonstrate the specific binding of the C-terminal acidic domain (AC-D) of the human TFIIEα subunit to the pleckstrin homology domain (PH-D) of the human TFIIH p62 subunit and describe the solution structures of the free and PH-D-bound forms of AC-D. Although the flexible N-terminal acidic tail from AC-D wraps around PH-D, the core domain of AC-D also interacts with PH-D. AC-D employs an entirely novel binding mode, which differs from the amphipathic helix method used by many transcriptional activators. So the binding surface between PH-D and AC-D is much broader than the specific binding surface between PH-D and the p53 acidic fragments. From our in vitro studies, we demonstrate that this interaction could be a switch to replace p53 with TFIIE on TFIIH in transcription

Gray-box modeling of 300 mm diameter Czochralski single-crystal Si production process

More than 95% of 300 mm diameter single-crystal silicon ingots, the raw material for semiconductors, are produced by the Czochralski process. The demand for improving yield, throughput, and control performance has been increasing. The present study developed a gray-box model that can predict controlled variables from manipulated variables with higher accuracy than the conventional first-principle model (Zheng et al., 2018), aiming at realizing model predictive control of the Czochralski process. The proposed gray-box model used a statistical model to predict the temperature gradient of the crystal at the solid–liquid interface , which was constant in the first-principle model. The crystal length and the melt temperature are used as the input variables to predict . The prediction accuracy of the proposed gray-box model was compared with that of the first-principle model using real process data obtained during the production of four silicon ingots. The results demonstrated that the proposed model reduced the root mean square errors of the crystal radius, the crystal growth rate, and the heater temperature by 94.1%, 62.7%, and 70.6% on average, respectively

Gray-box model-based predictive control of Czochralski process

The present study proposes a gray-box (GB) model-based predictive control method to produce high-quality 300 mm silicon ingots in the commercial Czochralski (CZ) process. The GB model consists of an energy transfer, hydrodynamic, and geometrical model and a statistical model, predicts three controlled variables, i.e., crystal radius, growth rate, and melt position, and represents the time-varying and nonlinear characteristics of the CZ process. Solving an optimization problem with the GB model requires heavy computational load; therefore, the proposed method derives the prediction model by successive linearization of the GB model to compute optimal manipulated variables in several seconds. The proposed method was compared with the conventional method using PID controllers in disturbance rejection performance through control simulations. The results have demonstrated that the integral absolute error (IAE) of the proposed method was reduced by 60% on average and 89% at maximum even when a plant-model mismatch exists

Factors affecting the willingness of nursing care staffs for cooperation with heart failure care and the role of internet video education

Abstract Background With the aging of heart failure (HF) patients, collaboration between medical and nursing care facilities is essential for HF care. The aims of this study were: (1) to identify the factors that affect willingness of nursing care staffs to cooperate with HF care; (2) to test whether the internet video education is useful in improving their willingness to collaborate. Methods A web‐based questionnaire was e‐mailed to 417 registered medical corporations that operated nursing care facilities in the prefecture where the authors work. Medical and care staff working at each facility were asked their willingness to cooperate with HF care and their problems about collaboration. Machine learning analysis was used to assess the factors associated with unwillingness to cooperate. After watching a 6‐min YouTube video explaining HF and community collaboration, we reaffirmed their willingness to cooperate. Results We received responses from 76 medical and care staff members. Before watching the video, 32.9% of participants stated that they were unwilling to cooperate with HF care. Machine learning analysis showed that job types, perceived problems of collaboration, and low opportunities to learn about HF were associated with unwillingness to cooperation. After watching the video, we observed an increase from 67.1% to 80.3% (p < 0.05) of participants willing to cooperate with HF care. Conclusions Job types, perceived problems of collaboration, and low opportunities to learn about HF are associated with unwillingness of nursing care staff for HF care. Internet videos are potential learning tool that can easily promote community collaboration for HF