No abstract

Gallic acid is an active phenolic acid widely distributed in plants, and there is compelling evidence to prove its anti-inflammatory effects. NLRP3 inflammasome dysregulation is closely linked to many inflammatory diseases. However, how gallic acid affects the NLRP3 inflammasome remains unclear. Therefore, in the present study, we investigated the mechanisms underlying the effects of gallic acid on the NLRP3 inflammasome and pyroptosis, as well as its effect on gouty arthritis in mice. The results showed that gallic acid inhibited lactate dehydrogenase (LDH) release and pyroptosis in lipopolysaccharide (LPS)-primed and ATP-, nigericin-, or monosodium urate (MSU) crystal-stimulated macrophages. Additionally, gallic acid blocked NLRP3 inflammasome activation and inhibited the subsequent activation of caspase-1 and secretion of IL-1β. Gallic acid exerted its inhibitory effect by blocking NLRP3-NEK7 interaction and ASC oligomerization, thereby limiting inflammasome assembly. Moreover, gallic acid promoted the expression of nuclear factor E2-related factor 2 (Nrf2) and reduced the production of mitochondrial ROS (mtROS). Importantly, the inhibitory effect of gallic acid could be reversed by treatment with the Nrf2 inhibitor ML385. NRF2 siRNA also abolished the inhibitory effect of gallic acid on IL-1β secretion. The results further showed that gallic acid could mitigate MSU-induced joint swelling and inhibit IL-1β and caspase 1 (p20) production in mice. Moreover, gallic acid could moderate MSU-induced macrophages and neutrophils migration into joint synovitis. In summary, we found that gallic acid suppresses ROS generation, thereby limiting NLRP3 inflammasome activation and pyroptosis dependent on Nrf2 signaling, suggesting that gallic acid possesses therapeutic potential for the treatment of gouty arthritis.

Rapid atrial pacing for long time periods induced sustained AF that can be eliminated by linear right and left atrial lesions created with RFCA, with preservation of sinus rhythm and atrial contractile function. Chronic AF increased the expression and distribution of gap junction protein Cx43, which became reduced in ablated and nearby nonablated areas.

Signet ring cell carcinoma is a type of rare adenocarcinoma with poor prognosis. Early detection leads to huge improvement of patients' survival rate. However, pathologists can only visually detect signet ring cells under the microscope. This procedure is not only laborious but also prone to omission. An automatic and accurate signet ring cell detection solution is thus important but has not been investigated before. In this paper, we take the first step to present a semi-supervised learning framework for the signet ring cell detection problem. Self-training is proposed to deal with the challenge of incomplete annotations, and cooperative-training is adapted to explore the unlabeled regions. Combining the two techniques, our semi-supervised learning framework can make better use of both labeled and unlabeled data. Experiments on large real clinical data demonstrate the effectiveness of our design. Our framework achieves accurate signet ring cell detection and can be readily applied in the clinical trails. The dataset will be released soon to facilitate the development of the area.

Cx43 undergoes both distribution and concentration changes following acute cardiac ischemia. The loss of Cx43 protein is heterogeneous. Cx43 dephosphorylation occurred within 1 hour following ischemia.

No abstract

Memristive logic device is a promising unit for beyond von Neumann computing systems and 2D materials are widely used because of their controllable interfacial properties. Most of these 2D memristive devices, however, are made from semiconducting chalcogenides which fail to gate the off‐state current. To this end, a crossbar device using 2D HfSe2 is fabricated, and then the top layers are oxidized into “high‐k” dielectric HfSexOy via oxygen plasma treatment, so that the cell resistance can be remarkably increased. This two‐terminal Ti/HfSexOy/HfSe2/Au device exhibits excellent forming‐free resistive switching performance with high switching speed (<50 ns), low operation voltage (<3 V), large switching window (103), and good data retention. Most importantly, the operation current and the power consumption reach 100 pA and 0.1 fJ to 0.1 pJ, much lower than other HfO based memristors. A functionally complete low‐power Boolean logic is experimentally demonstrated using the memristive device, allowing it in the application of energy‐efficient in‐memory computing.

The fully memristive neural network consisting of the threshold switching (TS) material-based electronic neurons and resistive switching (RS) one-based synapses shows the potential for revolutionizing the energy and area efficiency in neuromorphic computing while being confronted with challenges such as reliability and process compatibility between memristive synaptic and neuronal devices. Here, a spiking convolutional neural network (SCNN) is constructed with the forming-and-annealing-free V/VO x /HfWO x /Pt memristive devices. Specifically, both highly reliable RS (endurance >10 10 , on-off ratio >10 3 ) and TS (endurance >10 12 ) are found in the same device by setting it at RRAM or selector mode with either the HfWO x or naturally oxidized VO x layers dominating the conductance tuning. Such reconfigurability enables the emulation of both synaptic and nonpolar neuronal behaviors within the same device. A V/VO x /HfWO x /Pt-based hardware system is thus experimentally demonstrated at much simplified process complexity and higher reliability, in which typical neural dynamics including synaptic plasticity and nonpolar neuronal spiking response are imitated. At the network level, a fully memristive SCNN incorporating nonpolar neurons is proposed for the first time. The system level simulation shows competency in pattern recognition with a dramatically reduced hardware consumption, paving the way for implementing fully memristive intelligent systems.