The integrity of the intestinal barrier is critical for protecting the host against the pathogen. The role of hypoxia-inducible factor-1α (HIF-1α) in the intestinal barrier disfunction related to sepsis remained unclear. The purpose of the present study is to investigate the role of HIF-1α on oxidative damage, the intestinal mucosal permeability, structural and morphological changes during sepsis. Twenty-four Sprague Dawley (SD) rats were randomly divided into four groups of 6 rats each: the sham group (sham), sepsis group (subjected to cecal ligation and perforation, CLP), sepsis + DMOG group (40 mg/kg of DMOG by intraperitoneal injection for 7 consecutive days before CLP), and sepsis + BAY 87–2243 group (9 mg/kg of BAY 87–2243 orally administered for 3 consecutive days before CLP). Sepsis increased plasma levels of inflammatory mediators, oxidative stress markers and HIF-1α expression; caused pathological damage; increased permeability (P < 0.05); and decreased TJ protein expression in the intestinal mucosa of rats with sepsis (P < 0.05). The addition of DMOG up-regulated HIF-1α, then decreased the plasma levels of inflammatory mediators, oxidative stress markers, alleviated pathological damage to the intestinal mucosa and decreased intestinal permeability (P < 0.05); while BAY 87–2243 treatment had the opposite effects. Our findings showed that HIF-1α protects the intestinal barrier function of septic rats by inhibiting intestinal inflammation and oxidative damage, our results provide a novel insight for developing sepsis treatment.

BackgroundOxycodone can be used both intravenously and epidurally in elderly patients because of its strong analgesic effect and more slight respiratory inhibition compared with other opioids at the same effect. In this study, we determined the median effective concentration (EC50) of epidural ropivacaine required for great saphenous vein surgery in elderly patients in order to describe its pharmacodynamic interaction with oxycodone.MethodsOne hundred forty-one elderly patients scheduled for high ligation and stripping of the great saphenous vein surgery were allocated into three groups in a randomized, double-blinded manner as follows: Q2.5 group (2.5 mg oxycodone), Q5.0 group (5.0 mg oxycodone), and C group (normal saline). Anesthesia, was achieved with epidural ropivacaine and oxycodone. The EC50 of ropivacaine for surgery with different doses of oxycodone was adjusted by using an up-and-down sequential methods with an adjacent concentration gradient at a factor of 0.9 to inhibit analgesia. Anesthesia associated adverse events and recovery, characteristics were also recorded.ResultsThe EC50 of ropivacaine for the great saphenous vein surgery in elderly patients was 0.399% (95% CI, 0.371–0.430%) in the Q2.5 group, 0.396% (95% CI, 0.355–0.441%) in the Q5.0 group, and 0.487% (95% CI, 0.510–0.465%) in the C group, respectively (P &lt; 0.05). Specially, the EC50 of ropivacaine in the Q2.5 and Q5.0 groups was lower than that in the C group (P &lt; 0.01), But the difference between the Q2.5 group and the Q5.0 group was not significant (P &gt; 0.05). There was no significant difference in the Bromage score from the motor block examination, heart rate (HR) or mean arterial pressure (MAP) at each observation time point after epidural administration among the three groups (P &gt; 0.05). No serious adverse reactions occurred in any of the three groups.ConclusionOxycodone combined with ropivacaine epidural anesthesia can reduce the EC50 of ropivacaine required for elderly patients undergoing the great saphenous vein surgery. There was no significant difference in anesthesia associated adverse events among the three groups. The recommended dose of oxycodone is 2.5 mg.

Face recognition is a crucial task in various multimedia applications such as security check, credential access and motion sensing games. However, the task is challenging when an input face is noisy (e.g. poor-condition RGB image) or lacks certain information (e.g. 3D face without color). In this work, we propose a Multimodal Training Unimodal Test (MTUT) framework for robust face classification, which exploits the cross-modality relationship during training and applies it as a complementary of the imperfect single modality input during testing. Technically, during training, the framework (1) builds both intra-modality and cross-modality autoencoders with the aid of facial attributes to learn latent embeddings as multimodal descriptors, (2) proposes a novel multimodal embedding divergence loss to align the heterogeneous features from different modalities, which also adaptively avoids the useless modality (if any) from confusing the model. This way, the learned autoencoders can generate robust embeddings in single-modality face classification on test stage. We evaluate our framework in two face classification datasets and two kinds of testing input: (1) poor-condition image and (2) point cloud or 3D face mesh, when both 2D and 3D modalities are available for training. We experimentally show that our MTUT framework consistently outperforms ten baselines on 2D and 3D settings of both datasets 1 .

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