Search

IntroductionInterleukin-18 (IL-18), a pro-inflammatory cytokine belonging to the IL-1 Family, is a key mediator ofautoinflammatory diseases associated with the development of macrophage activation syndrome (MAS).High levels of IL-18 correlate with MAS and COVID-19 severity and mortality, particularly in COVID-19patients with MAS. As an inflammation inducer, IL-18 binds its receptor IL-1 Receptor 5 (IL-1R5), leadingto the recruitment of the co-receptor, IL-1 Receptor 7 (IL-1R7). This heterotrimeric complex subsequentlyinitiates downstream signaling, resulting in local and systemic inflammation.MethodsWe reported earlier the development of a novel humanized monoclonal anti-human IL-1R7 antibody whichspecifically blocks the activity of human IL-18 and its inflammatory signaling in human cell and wholeblood cultures. In the current study, we further explored the strategy of blocking IL-1R7 inhyperinflammation in vivo using animal models.ResultsWe first identified an anti-mouse IL-1R7 antibody that significantly suppressed mouse IL-18 andlipopolysaccharide (LPS)-induced IFNg production in mouse splenocyte and peritoneal cell cultures. Whenapplied in vivo, the antibody reduced Propionibacterium acnes and LPS-induced liver injury and protectedmice from tissue and systemic hyperinflammation. Importantly, anti-IL-1R7 significantly inhibited plasma,liver cell and spleen cell IFNg production. Also, anti-IL-1R7 downregulated plasma TNFa, IL-6, IL-1b,MIP-2 production and the production of the liver enzyme ALT. In parallel, anti-IL-1R7 suppressed LPSinducedinflammatory cell infiltration in lungs and inhibited the subsequent IFNg production andinflammation in mice when assessed using an acute lung injury model.DiscussionAltogether, our data suggest that blocking IL-1R7 represents a potential therapeutic strategy to specificallymodulate IL-18-mediated hyperinflammation, warranting further investigation of its clinical application intreating IL-18-mediated diseases, including MAS and COVID-19.

Resistive Random-Access Memory (RRAM) presents a transformative technology for diverse computing and artificial intelligence applications. However, variability in the high resistance state (HRS) has proved to be a challenge, impeding its widespread adoption. This study focuses on optimizing TaOx-based RRAMs by strategically placing a nitrogen-doped TaOx barrier-layer (BL) to mitigate variability in the HRS. Through comprehensive electrical characterization and measurements, we uncover the critical influence of BL positioning on HRS variability and identify the optimal location of the BL to achieve a 2x lowering of HRS variability as well as an expanded range of operating voltages. Incremental reset pulse amplitude measurements show that the TaOx:N maintains a low HRS variability even at higher operating voltages when the position of the BL is optimized. Our findings offer insights into stable and reliable RRAM operation, highlighting the potential of the proposed BL to enhance the functionality of TaOx-based RRAMs and elevate overall device performance.

Abstract Analog hardware-based training provides a promising solution to developing state-of-the-art power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive random access memory (RRAM) has the potential to provide a low power alternative. The training accuracy of analog hardware depends on RRAM switching properties including the number of discrete conductance states and conductance variability. Furthermore, the overall power consumption of the system inversely correlates with the RRAM devices conductance. To study material dependence of these properties, TaOx and HfOx RRAM devices in one-transistor one-RRAM configuration (1T1R) were fabricated using a custom 65 nm CMOS fabrication process. Analog switching performance was studied with a range of initial forming compliance current (200–500 µA) and analog switching tests with ultra-short pulse width (300 ps) was carried out. We report that by utilizing low current during electroforming and high compliance current during analog switching, a large number of RRAM conductance states can be achieved while maintaining low conductance state. While both TaOx and HfOx could be switched to more than 20 distinct states, TaOx devices exhibited 10× lower conductance, which reduces total power consumption for array-level operations. Furthermore, we adopted an analog, fully in-memory training algorithm for system-level training accuracy benchmarking and showed that implementing TaOx 1T1R cells could yield an accuracy of up to 96.4% compared to 97% for the floating-point arithmetic baseline, while implementing HfOx devices would yield a maximum accuracy of 90.5%. Our experimental work and benchmarking approach paves the path for future materials engineering in analog-AI hardware for a low-power environment training.

The activation of CD40-mediated signaling in antigen-presenting cells is a promising therapeutic strategy to promote immune responses against tumors. Most agonistic anti-CD40 antibodies currently in development require the Fcγ-receptor (FcγR)-mediated crosslinking of CD40 molecules for a meaningful activation of CD40 signaling but have limitations due to dose-limiting toxicities. Here we describe the identification of CD40 antibodies which strongly stimulate antigen-presenting cells in an entirely FcγR-independent manner. These Fc-silenced anti-CD40 antibodies induce an efficient upregulation of costimulatory receptors and cytokine release by dendritic cells. Finally, the most active identified anti-CD40 antibody shows activity in humanized mice. More importantly, there are no signs of obvious toxicities. These studies thus demonstrate the potent activation of antigen-presenting cells with anti-CD40 antibodies lacking FcγR-binding activity and open the possibility for an efficacious and safe combination therapy for cancer patients.

The predictive capability of existing physical descriptions of multi-state devices (e.g., oxide memristors, ferroelectrics, antiferroelectric, etc.) cannot be fully leveraged in circuit simulations due to practical limitations regarding the complexity of compact models. We attempt to circumvent this issue by adopting a machine-learning (ML) - based approach to develop a compact model that retains the full physical description of these devices. ML-based modeling approaches have garnered immense interest in recent years and have already been successfully utilized in making models for several novel devices. A known hurdle for ML-based compact modeling is the need for a large amount of experimental data to properly train the model. We propose a method to simulate additional data by duplicating the data and adding Gaussian Noise to the duplicates. We propose a generalized framework to - (i) facilitate efficient training of ML-based device models, (ii) conduct seamless conversion to a Verilog-A model, and (iii) interface with industry-standard circuit simulators (HSPICE, SPECTRE, etc.). We demonstrate the capabilities of our framework using the hafnium oxide (HfOx) memristor as a test device. As the source of the training data, we use a physical model that unifies detailed atomic-level descriptions with self-consistent evaluation of electronic transport. In addition, we test our model with experimental data for multiple memristor samples and repeated cycles of the same sample. Our ML-based framework prepares a circuit-compatible compact model to facilitate system-level simulations. With our model, we achieve a root mean squared error (RMSE) of 0.000863 and an R2 of 0.977371 on our testing data.