The role of IMP dehydrogenase 2 in Inauhzin-induced ribosomal stress

The ‘ribosomal stress (RS)-p53 pathway’ is triggered by any stressor or genetic alteration that disrupts ribosomal biogenesis, and mediated by several ribosomal proteins (RPs), such as RPL11 and RPL5, which inhibit MDM2 and activate p53. Inosine monophosphate (IMP) dehydrogenase 2 (IMPDH2) is a rate-limiting enzyme in de novo guanine nucleotide biosynthesis and crucial for maintaining cellular guanine deoxy- and ribonucleotide pools needed for DNA and RNA synthesis. It is highly expressed in many malignancies. We previously showed that inhibition of IMPDH2 leads to p53 activation by causing RS. Surprisingly, our current study reveals that Inauzhin (INZ), a novel non-genotoxic p53 activator by inhibiting SIRT1, can also inhibit cellular IMPDH2 activity, and reduce the levels of cellular GTP and GTP-binding nucleostemin that is essential for rRNA processing. Consequently, INZ induces RS and the RPL11/RPL5-MDM2 interaction, activating p53. These results support the new notion that INZ suppresses cancer cell growth by dually targeting SIRT1 and IMPDH2

Generalizing across Temporal Domains with Koopman Operators

In the field of domain generalization, the task of constructing a predictive model capable of generalizing to a target domain without access to target data remains challenging. This problem becomes further complicated when considering evolving dynamics between domains. While various approaches have been proposed to address this issue, a comprehensive understanding of the underlying generalization theory is still lacking. In this study, we contribute novel theoretic results that aligning conditional distribution leads to the reduction of generalization bounds. Our analysis serves as a key motivation for solving the Temporal Domain Generalization (TDG) problem through the application of Koopman Neural Operators, resulting in Temporal Koopman Networks (TKNets). By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains. Through empirical evaluations conducted on synthetic and real-world datasets, we validate the effectiveness of our proposed approach.Comment: 15 pages, 7 figures, Accepted by AAAI 2024. arXiv admin note: text overlap with arXiv:2206.0004

Arterial Embolization Hyperthermia Using As2O3 Nanoparticles in VX2 Carcinoma–Induced Liver Tumors

BACKGROUND: Combination therapy for arterial embolization hyperthermia (AEH) with arsenic trioxide (As(2)O(3)) nanoparticles (ATONs) is a novel treatment for solid malignancies. This study was performed to evaluate the feasibility and therapeutic effect of AEH with As(2)O(3) nanoparticles in a rabbit liver cancer model. The protocol was approved by our institutional animal use committee. METHODOLOGY/PRINCIPAL FINDINGS: In total, 60 VX(2) liver-tumor-bearing rabbits were randomly assigned to five groups (n = 12/group) and received AEH with ATONs (Group 1), hepatic arterial embolization with ATONs (Group 2), lipiodol (Group 3), or saline (Group 4), on day 14 after tumor implantation. Twelve rabbits that received AEH with ATONs were prepared for temperature measurements, and were defined as Group 5. Computed tomography was used to measure the tumors' longest dimension, and evaluation was performed according to the Response Evaluation Criteria in Solid Tumors. Hepatic toxicity, tumor necrosis rate, vascular endothelial growth factor level, and microvessel density were determined. Survival rates were measured using the Kaplan-Meier method. The therapeutic temperature (42.5°C) was obtained in Group 5. Hepatotoxicity reactions occurred but were transient in all groups. Tumor growth was delayed and survival was prolonged in Group 1 (treated with AEH and ATONs). Plasma and tumor vascular endothelial growth factor and microvessel density were significantly inhibited in Group 1, while tumor necrosis rates were markedly enhanced compared with those in the control groups. CONCLUSIONS: ATON-based AEH is a safe and effective treatment that can be targeted at liver tumors using the dual effects of hyperthermia and chemotherapy. This therapy can delay tumor growth and noticeably inhibit tumor angiogenesis

Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment

We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiographs and free-text radiology reports exist, only limited numerical edema severity labels can be extracted from radiology reports. This is a significant challenge in learning such models for image classification. To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time. Our experimental results suggest that the joint image-text representation learning improves the performance of pulmonary edema assessment compared to a supervised model trained on images only. We also show the use of the text for explaining the image classification by the joint model. To the best of our knowledge, our approach is the first to leverage free-text radiology reports for improving the image model performance in this application. Our code is available at: https://github.com/RayRuizhiLiao/joint_chestxray.NIH/NIBIB/NAC (Grant P41EB015902