Inhibition of nuclear factor-κB by 6-O-acetyl shanzhiside methyl ester protects brain against injury in a rat model of ischemia and reperfusion

<p>Abstract</p> <p>Background</p> <p>Recent studies have demonstrated an inflammatory response associated with the pathophysiology of cerebral ischemia. The beneficial effects of anti-inflammatory drugs in cerebral ischemia have been documented. When screening natural compounds for drug candidates in this category, we isolated 6-O-acetyl shanzhiside methyl ester (ND02), an iridoid glucoside compound, from the leaves of <it>Lamiophlomis rotata (Benth.) Kudo</it>. The objectives of this study were to determine the effects of ND02 on a cultured neuronal cell line, SH-SY5Y, in vitro, and on experimental ischemic stroke in vivo.</p> <p>Methods</p> <p>For TNF-α-stimulated SH-SY5Y cell line experiments in vitro, SH-SY5Y cells were pre-incubated with ND02 (20 μM or 40 μM) for 30 min and then incubated with TNF-α (20 ng/ml) for 15 min. For in vivo experiments, rats were subjected to middle cerebral artery occlusion (MCAO) for 1 h followed by reperfusion for 23 h.</p> <p>Results</p> <p>ND02 treatment of SH-SY5Y cell lines blocked TNF-α-induced nuclear factor-κB (NF-κB) and IκB-α phosphorylation and increased Akt phosphorylation. LY294002 blocked TNF-α-induced phosphorylation of Akt and reduced the phosphorylation of both IκB-α and NF-κB. At doses higher than 10 mg/kg, ND02 had a significant neuroprotective effect in rats with cerebral ischemia and reperfusion (I/R). ND02 (25 mg/kg) demonstrated significant neuroprotective activity even after delayed administration 1 h, 3 h and 5 h after I/R. ND02, 25 mg/kg, attenuated histopathological damage, decreased cerebral Evans blue extravasation, inhibited NF-κB activation, and enhanced Akt phosphorylation.</p> <p>Conclusion</p> <p>These data show that ND02 protects brain against I/R injury with a favorable therapeutic time-window by alleviating cerebral I/R injury and attenuating blood-brain barrier (BBB) breakdown, and that these protective effects may be due to blocking of neuronal inflammatory cascades through an Akt-dependent NF-κB signaling pathway.</p

Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction

As our planet is entering into the "global boiling" era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding regional dynamical models or statistical downscaling frameworks, are often susceptible to the influence of downscaling uncertainty. Here, we address these limitations by introducing a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1{\deg} to 0.1{\deg} resolution. Compared with deterministic downscaling schemes, it not only has more accurate local details, but also can generate a large number of ensemble members based on probability distribution sampling to evaluate the uncertainty of downscaling. Additionally, we apply the model to generate a 180-year dataset of monthly surface variables in East Asia, offering a more detailed perspective for understanding local scale climate change over the past centuries

A lightweight Yunnan Xiaomila detection and pose estimation based on improved YOLOv8

IntroductionYunnan Xiaomila is a pepper variety whose flowers and fruits become mature at the same time and multiple times a year. The distinction between the fruits and the background is low and the background is complex. The targets are small and difficult to identify.MethodsThis paper aims at the problem of target detection of Yunnan Xiaomila under complex background environment, in order to reduce the impact caused by the small color gradient changes between xiaomila and background and the unclear feature information, an improved PAE-YOLO model is proposed, which combines the EMA attention mechanism and DCNv3 deformable convolution is integrated into the YOLOv8 model, which improves the model’s feature extraction capability and inference speed for Xiaomila in complex environments, and achieves a lightweight model. First, the EMA attention mechanism is combined with the C2f module in the YOLOv8 network. The C2f module can well extract local features from the input image, and the EMA attention mechanism can control the global relationship. The two complement each other, thereby enhancing the model’s expression ability; Meanwhile, in the backbone network and head network, the DCNv3 convolution module is introduced, which can adaptively adjust the sampling position according to the input feature map, contributing to stronger feature capture capabilities for targets of different scales and a lightweight network. It also uses a depth camera to estimate the posture of Xiaomila, while analyzing and optimizing different occlusion situations. The effectiveness of the proposed method was verified through ablation experiments, model comparison experiments and attitude estimation experiments.ResultsThe experimental results indicated that the model obtained an average mean accuracy (mAP) of 88.8%, which was 1.3% higher than that of the original model. Its F1 score reached 83.2, and the GFLOPs and model sizes were 7.6G and 5.7MB respectively. The F1 score ranked the best among several networks, with the model weight and gigabit floating-point operations per second (GFLOPs) being the smallest, which are 6.2% and 8.1% lower than the original model. The loss value was the lowest during training, and the convergence speed was the fastest. Meanwhile, the attitude estimation results of 102 targets showed that the orientation was correctly estimated exceed 85% of the cases, and the average error angle was 15.91°. In the occlusion condition, 86.3% of the attitude estimation error angles were less than 40°, and the average error angle was 23.19°.DiscussionThe results show that the improved detection model can accurately identify Xiaomila targets fruits, has higher model accuracy, less computational complexity, and can better estimate the target posture

Efficacy of calcium dobesilate in treating Chinese patients with mild-to-moderate non-proliferative diabetic retinopathy (CALM-DR): protocol for a single-blind, multicentre, 24-armed cluster-randomised, controlled trial.

INTRODUCTION Calcium dobesilate (CaD) has been used in the treatment of diabetic retinopathy (DR) due to its potential in protecting against retinal vascular damage. However, there is limited evidence exploring its efficacy in combating DR progression. This study is aimed at evaluating whether CaD could prevent DR progression into an advanced stage among Chinese patients with mild-to-moderate non-proliferative DR (NPDR). METHODS AND ANALYSIS This study is a single-blind, multicentre, cluster-randomised, controlled superiority trial. A total of 1272 patients with mild-to-moderate NPDR will be enrolled and randomly assigned at a 1:1 ratio into the control group (conventional treatment group) and the intervention group (conventional treatment plus CaD (500 mg three times per day) for 12 months). Patients will be followed at 1, 3, 6 and 12 months after randomisation and receiving treatments, with the severity of DR assessed by the Early Treatment Diabetic Retinopathy Study (ETDRS) scale. The primary endpoint is the progression of DR during follow-up, which is defined as an increase of two or more steps in the ETDRS scale. The secondary endpoints include the concomitant changes in visual acuity, presence, number, location and type of retinal lesions, and retinal blood vessel diameter as well as the arteriovenous ratio at different visits. ETHICS AND DISSEMINATION Each local ethics committee (first Vote: Ethical Review Committees of Zhongda Hospital of Southeast University (2019ZDSYLL132-P01)) has approved the study. The results will be published in high impact peer-reviewed scientific journals aimed at the general reader. TRIAL REGISTRATION NUMBERS NCT04283162

CRISPR-Cas and catalytic hairpin assembly technology for target-initiated amplification detection of pancreatic cancer specific tsRNAs

Transfer RNA-derived small RNAs (tsRNAs) tRF-LeuCAG-002 (ts3011a RNA) is a novel class of non-coding RNAs biomarker for pancreatic cancer (PC). Reverse transcription polymerase chain reaction (RT-qPCR) has been unfit for community hospitals that are short of specialized equipment or laboratory setups. It has not been reported whether isothermal technology can be used for detection, because the tsRNAs have rich modifications and secondary structures compared with other non-coding RNAs. Herein, we have employed a catalytic hairpin assembly (CHA) circuit and clustered regularly interspaced short palindromic repeats (CRISPR) to develop an isothermal and target-initiated amplification method for detecting ts3011a RNA. In the proposed assay, the presence of target tsRNA triggers the CHA circuit that transforms new DNA duplexes to activate collateral cleavage activity of CRISPR-associated proteins (CRISPR-Cas) 12a, achieving cascade signal amplification. This method showed a low detection limit of 88 aM at 37 °C within 2 h. Moreover, it was demonstrated for the first time that, this method is less likely to produce aerosol contamination than RT-qPCR by simulating aerosol leakage experiments. This method has good consistency with RT-qPCR in the detection of serum samples and showed great potential for PC-specific tsRNAs point-of-care testing (POCT)

EMP3 Overexpression in Primary Breast Carcinomas is not Associated with Epigenetic Aberrations

Epithelial membrane protein 3 (EMP3) is a trans-membrane signaling molecule with important roles in the regulation of apoptosis, differentiation and invasion of cancer cells, but the detailed is largely still unknown. We analyzed the mRNA levels and methylation statuses of EMP3 in 63 primary breast carcinomas and assessed their correlations with clinicopathologic variables. The expression of EMP3 mRNA in primary breast carcinomas was significantly higher than the expression of 20 normal breast tissues (p<10-7). EMP3 overexpression in breast carcinomas was significantly related to histological grade III (p=3.9×10-7), lymph node metastasis (p=0.003), and strong Her-2 expression (p=3.3×10-6). Hypermethylation frequencies of EMP3 were detected in 36.5% of breast carcinomas by methylation-specific polymerase chain reaction. However, no significant correlations were found between methylation status of EMP3 and mRNA expression levels as well as other clinical parameters. In conclusion, EMP3 may be a novel marker of tumor aggressiveness. Overexpression of EMP3 in primary breast carcinoma is not associated with DNA methylation