Spinal Astrocytic Activation Is Involved in a Virally-Induced Rat Model of Neuropathic Pain

Postherpetic neuralgia (PHN), the most common complication of herpes zoster (HZ), plays a major role in decreased life quality of HZ patients. However, the neural mechanisms underlying PHN remain unclear. Here, using a PHN rat model at 2 weeks after varicella zoster virus infection, we found that spinal astrocytes were dramatically activated. The mechanical allodynia and spinal central sensitization were significantly attenuated by intrathecally injected L-α-aminoadipate (astrocytic specific inhibitor) whereas minocycline (microglial specific inhibitor) had no effect, which indicated that spinal astrocyte but not microglia contributed to the chronic pain in PHN rat. Further study was taken to investigate the molecular mechanism of astrocyte-incudced allodynia in PHN rat at post-infection 2 weeks. Results showed that nitric oxide (NO) produced by inducible nitric oxide synthase mediated the development of spinal astrocytic activation, and activated astrocytes dramatically increased interleukin-1β expression which induced N-methyl-D-aspartic acid receptor (NMDAR) phosphorylation in spinal dorsal horn neurons to strengthen pain transmission. Taken together, these results suggest that spinal activated astrocytes may be one of the most important factors in the pathophysiology of PHN and “NO-Astrocyte-Cytokine-NMDAR-Neuron” pathway may be the detailed neural mechanisms underlying PHN. Thus, inhibiting spinal astrocytic activation may represent a novel therapeutic strategy for clinical management of PHN

Silencing COI1 in Rice Increases Susceptibility to Chewing Insects and Impairs Inducible Defense

The jasmonic acid (JA) pathway plays a key role in plant defense responses against herbivorous insects. CORONATINE INSENSITIVE1 (COI1) is an F-box protein essential for all jasmonate responses. However, the precise defense function of COI1 in monocotyledonous plants, especially in rice (Oryza sativa L.) is largely unknown. We silenced OsCOI1 in rice plants via RNA interference (RNAi) to determine the role of OsCOI1 in rice defense against rice leaf folder (LF) Cnaphalocrocis medinalis, a chewing insect, and brown planthopper (BPH) Nilaparvata lugens, a phloem-feeding insect. In wild-type rice plants (WT), the transcripts of OsCOI1 were strongly and continuously up-regulated by LF infestation and methyl jasmonate (MeJA) treatment, but not by BPH infestation. The abundance of trypsin protease inhibitor (TrypPI), and the enzymatic activities of polyphenol oxidase (PPO) and peroxidase (POD) were enhanced in response to both LF and BPH infestation, but the activity of lipoxygenase (LOX) was only induced by LF. The RNAi lines with repressed expression of OsCOI1 showed reduced resistance against LF, but no change against BPH. Silencing OsCOI1 did not alter LF-induced LOX activity and JA content, but it led to a reduction in the TrypPI content, POD and PPO activity by 62.3%, 48.5% and 27.2%, respectively. In addition, MeJA-induced TrypPI and POD activity were reduced by 57.2% and 48.2% in OsCOI1 RNAi plants. These results suggest that OsCOI1 is an indispensable signaling component, controlling JA-regulated defense against chewing insect (LF) in rice plants, and COI1 is also required for induction of TrypPI, POD and PPO in rice defense response to LF infestation

Formation mechanism and control of flaring in forward tube spinning

Forward tube spinning (or flow forming) is usually employed to produce cylindrically tubular components to meet the increasing requirements for manufacturing high-performance and light-weight products at low cost and short lead-time. In forward tube spinning, flaring defect may easily occur at the opening end of tubes, which would deteriorate the quality of the spun tubular parts and reduce the material utilization. In addition, an additional operation is needed to trim away the flared end of the spun tabular parts. Efficient control of flaring formation is thus a non-trivial issue in forward tube spinning process and thus become one of the critical bottleneck issues to be addressed in this unique forming process. In this study, the formation mechanism of flaring was systematically studied via finite element (FE) simulation and an in-depth understanding was thus established, which forms basis for control of flaring forming in forward tube spinning. Based on the simulated material flow behaviour, it is found that flaring is formed by the material in non-spun zone flowing away from the mandrel. This material flow behaviour is caused by the pile up and the decreasing stiffness of the non-spun zone. In addition, the effects of process parameters on flaring were investigated to reduce flaring. The results show that the smaller feed rate and thickness reduction per pass can reduce the maximum flaring to a certain extent, but is very limited. To increase productivity and shorten forming lead-time, an efficient method to control flaring was proposed using a pressing ring in front of the roller based on the formation mechanism of flaring. FE simulation was further used to study the feasibility and demonstrates the validity of the method in terms of reducing and even eliminating the flaring with a short production lead-time. Finally, the forward tube spinning experiments were carried out to validate the formation mechanism of flaring and the method to avoid or eliminate the flaring formation in forward tube spinning

Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data

For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient's 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis