Inhibiting effect of Radix Hedysari Polysaccharide (HPS) on endotoxin-induced uveitis in rats

PurposeThe aim of this study is to investigate the anti-inflammatory effect of Radix Hedysari Polysaccharide (HPS) on clinical indicators, the expression of Toll-like receptor-4 (TLR4) and its downstream transduction molecules during endotoxin-induced uveitis in rats.MethodsEIU was induced through the intraperitoneal injection of male Wistar rats with lipopolysaccharide (LPS 200 μg). HPS (400 mg/kg), DXM (1 mg/kg) or an equivalent volume of normal saline was injected intraperitoneally 1 h before the LPS induction. The clinical manifestation was observed and scored at 2-h intervals using a slit microscope. The degree of inflammatory reaction was determined by routine histological examinations, and the expression of TLR4 and MyD88 in the iris-ciliary body complex was detected through a double-labeled immunofluorescence study. Real-time RT-PCR was used to assess the effects of HPS on the expression of the TLR4 complex, MyD88 and NF-κB p65 mRNA. The protein expression levels of TLR4, MyD88 and NF-κB p65 were examined by western blot.ResultsHPS treatment produced similar therapeutic results with dexamethasone by significantly reducing the clinical severity of EIU as well as fibrin exudations and inflammatory cell infiltration in the eye. Correspondingly, according to the immunofluorescence results, HPS treatment significantly suppressed the expression of TLR4 and MyD88 in the iris–ciliary body complex. HPS treatment could also remarkably reduce the mRNA and protein expression of the TLR4 complex, MyD88 and NF-κB p65.ConclusionHPS can suppress the intraocular inflammation observed in EIU by inhibiting TLR4 and its downstream signal transduction pathway.     

Effect of hesperidin and neohesperidin from bittersweet orange (Citrus aurantium var. bigaradia) peel on indomethacin-induced peptic ulcers in rats

Hesperidin and neohesperidin are the major flavanones isolated from bittersweet orange. It was recently reported that they have potent anti-inflammatory effects in various inflammatory models. In the present study, the effects of hesperidin and neohesperidin on indomethacin-induced ulcers in rats and the underlying mechanisms were investigated. Gastric ulcers were induced in rats with a single dose of indomethacin. The effects of pretreatment with hesperidin and neohesperidin were assessed in comparison with omeprazole as reference standard. Ulcer index, gene expression of gastric cyclooxygenase-2 (COX-2), tumor necrosis factor alpha (TNF-α), lipid peroxidation product, malondialdhyde (MDA), and reduced glutathione (GSH) content in stomach were measured. Furthermore, gross and histopathological examination was performed. Our results indicated that both hesperidin and neohesperidin significantly aggravated gastric damage caused by indomethacin administration as evidenced by increased ulcer index and histopathological changes of stomach.     

The capacity and effectiveness of diosmectite and charcoal in trapping the compounds causing the most frequent intoxications in acute medicine: A comparative study

The aim of the study was to compare the adsorption ability of two adsorbent materials, namely diosmectite and activated charcoal towards selected model compounds that are most commonly involved in acute intoxication. Eleven model compounds were selected: acetylsalicylic acid, α-amanitin, amlodipine, digoxin, phenobarbital, ibuprofen, imipramine, carbamazepine, oxazepam, promethazine, and theophylline. Of the tested compounds, promethazine and imipramine were the most effectively adsorbed to diosmectite. Their adsorption to diosmectite (0.356 ± 0.029 mg promethazine/mg diosmectite and 0.354 ± 0.019 mg imipramine/mg diosmectite, respectively) was significantly higher than their adsorption to activated charcoal. The effect of temperature and pH on the adsorption efficiencies was also evaluated. In the case of experiments with mixture of both adsorbents, they mostly behaved in a solution independently or in a slightly antagonistic way. Using various methods such as N₂ adsorption and thermogravimetric analysis, the structure and texture of diosmectite and activated charcoal were attained.     

Evaluation of Ebola spreading in West Africa and decision of optimal medicine delivery strategies based on mathematical models

This paper mainly focuses on Ebola prediction and optimal location of emergency supplies delivery in Guinea, Liberia and Sierra Leone. First, an epidemic model called SEIIRHF model which takes the susceptible, the exposed, the infected, the recovered, the ones in hospital and the buried into consideration is developed to evaluate Ebola propagation. In this model, the infected and the ones in hospital are divided into two categories, serious infected and not serious, to simulate Ebola patients who are in ICU or not. Data of total and death cases from WHO website are used to curve fit. Then the curve is tested by data acquired recently. According to the curve, we predict there are 4000 total cases and 2500 death cases in Guinea, 9509 total cases and 4166 death cases in Liberia, 12,556 total cases and 3933 death cases in Sierra Leone by end of June, 2015. Then Clustering method is adopted to divide the most serious regions in a country into several parts, each part has a location of medicine delivery. Then two circular routes in different directions are decided by using TSP (Traveling Salesman Problem) method with consideration of medicine can be delivered within several hours by a car. Thus, DUBREKA, KOUROUSSA, and MACENTA in Guinea, Grand Bassa in Liberia and Tonkolili in Sierra Leone are selected as location of delivery respectively. Besides, Guinea has 6 delivery routes, Liberia has 2 delivery routes and Sierra Leone has 2 delivery routes.     

Rubik’s Cube+: A self-supervised feature learning framework for 3D medical image analysis

Due to the development of deep learning, an increasing number of research works have been proposed to establish automated analysis systems for 3D volumetric medical data to improve the quality of patient care. However, it is challenging to obtain a large number of annotated 3D medical data needed to train a neural network well, as such manual annotation by physicians is time consuming and laborious. Self-supervised learning is one of the potential solutions to mitigate the strong requirement of data annotation by deeply exploiting raw data information. In this paper, we propose a novel self-supervised learning framework for volumetric medical data. Specifically, we propose a pretext task, i.e., Rubik’s cube+, to pre-train 3D neural networks. The pretext task involves three operations, namely cube ordering, cube rotating and cube masking, forcing networks to learn translation and rotation invariant features from the original 3D medical data, and tolerate the noise of the data at the same time. Compared to the strategy of training from scratch, fine-tuning from the Rubik’s cube+ pre-trained weights can remarkablely boost the accuracy of 3D neural networks on various tasks, such as cerebral hemorrhage classification and brain tumor segmentation, without the use of extra data.     

Preoperative neutrophil-to-lymphocyte ratio is a more valuable prognostic factor than platelet-to-lymphocyte ratio for nonmetastatic rectal cancer

Several combinations of inflammatory factors, including neutrophil-to- lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), have been reported to be prognostic factors in various malignant tumors, including colorectal cancer (CRC). The aim of this study was to evaluate the prognostic value of NLR and PLR for patients with rectal cancer (RC) who underwent curative surgery. Data from patients who underwent curative resection for RC were retrospectively reviewed. The cutoff for NLR and PLR was defined as 2.3 and 144 by receiver operating characteristic (ROC) curve. Overall survival (OS) and disease-free survival (DFS) were assessed using Kaplan-Meier method. Multivariable Cox regression model was used to evaluate the independent prognostic significance of variables. A total of 140 patients were eligible in the study. High NLR (> 2.3) and high PLR (> 144) both predicted lower OS and DFS according to Kaplan-Meier method. But in the multivariable Cox regression model, only the high NLR retained significance for reduced OS and DFS. According to Chi-square test, patients with higher NLR had larger tumor size and higher pN-stage. While PLR was only associated with the pN-stage. High preoperative NLR was shown to be a negative independent prognostic factor in patients undergoing resection for nonmetastatic RC. It may be helpful as a factor to guide the postoperative therapies.