Understanding Haemophilus parasuis infection in porcine spleen through a transcriptomics approach

<p>Abstract</p> <p>Background</p> <p><it>Haemophilus parasuis </it>(HPS) is an important swine pathogen that causes Glässer's disease, which is characterized by fibrinous polyserositis, meningitis and arthritis. The molecular mechanisms that underlie the pathogenesis of the disease remain poorly understood, particularly the resistance of porcine immune system to HPS invasion. In this study, we investigated the global changes in gene expression in the spleen following HPS infection using the Affymetrix Porcine Genechip™.</p> <p>Results</p> <p>A total of 931 differentially expressed (DE) transcripts were identified in the porcine spleen 7 days after HPS infection; of these, 92 unique genes showed differential expression patterns based on analysis using BLASTX and Gene Ontology. The DE genes involved in the immune response included genes for inflammasomes (<it>RETN</it>, <it>S100A8</it>, <it>S100A9</it>, <it>S100A12</it>), adhesion molecules (<it>CLDN3</it>, <it>CSPG2</it>, <it>CD44</it>, <it>LGALS8</it>), transcription factors (<it>ZBTB16</it>, <it>SLC39A14</it>, <it>CEBPD</it>, <it>CEBPB</it>), acute-phase proteins and complement (<it>SAA1</it>, <it>LTF</it>, <it>HP</it>, <it>C3</it>), differentiation genes for epithelial cells and keratinocytes (<it>TGM1</it>, <it>MS4A8B</it>, <it>CSTA</it>), and genes related to antigen processing and presentation (<it>HLA-B</it>, <it>HLA-DRB1</it>). Further immunostimulation analyses indicated that mRNA levels of <it>S100A8</it>, <it>S100A9</it>, and <it>S100A12 </it>in porcine PK-15 cells increased within 48 h and were sustained after administration of lipopolysaccharide (LPS) and Poly(I:C) respectively. In addition, mapping of DE genes to porcine health traits QTL regions showed that 70 genes were distributed in 7 different known porcine QTL regions. Finally, 10 DE genes were validated by quantitative PCR.</p> <p>Conclusion</p> <p>Our findings demonstrate previously unrecognized changes in gene transcription that are associated with HPS infection <it>in vivo</it>, and many potential cascades identified in the study clearly merit further investigation. Our data provide new clues to the nature of the immune response in mammals, and we have identified candidate genes that are related to resistance to HPS.</p

Tang-Tong-Fang Confers Protection against Experimental Diabetic Peripheral Neuropathy by Reducing Inflammation

Tang-tong-fang (TTF) is a Chinese herbal formula that has been shown to be beneficial in diabetic peripheral neuropathy (DPN), a common complication secondary to diabetic microvascular injury. However, the underlying mechanism of protection in nerve ischemia provided by TTF is still unclear. We hypothesized that TTF alleviates DPN via inhibition of ICAM-1 expression. Therefore, we tested the effect of TTF in a previously established DPN model, in which nerve injury was induced by ischemia/reperfusion in streptozotocin-induced diabetic rats. We found that the conduction velocity and amplitude of action potentials of sciatic nerve conduction were reduced in the DPN model group but were rescued by TTF treatment. In addition, TTF treatment also attenuated the effect of DPN on other parameters including histology and ultrastructural changes, expression of ICAM-1, MPO, and TNF-α in rat sciatic nerves, and plasma sICAM-1 and MPO levels. Together, our data suggest that TTF treatment may alleviate DPN via ICAM-1 inhibition

Physical Human-Robot Interaction of a Robotic Exoskeleton By Admittance Control

In this paper, physical human-robot interaction (pHRI) approach is presented for the developed robotic exoskeleton using admittance control to deal with human subject's intention as well as the unknown inertia masses and moments in the robotic dynamics. Human subject's intention is represented by the reference trajectory when the robotic exoskeleton is complying with the external interaction force. Online estimation of the stiffness is employed to deal with the variable impedance property of the robotic exoskeleton. Admittance control is firstly presented based on the measured force in order to generate a reference trajectory in interaction tasks. Then adaptive control is proposed to deal with the uncertain robotic dynamics and a stability criterion can be obtained. Bounded errors are shown in the motion tracking while the robustness of the variable stiffness control is guaranteed. The experimental results indicate that the proposed control enables the human subjects to execute an admittance control task on the exoskeleton robot effectively

The role of Chinese herbal medicine in the treatment of diabetic nephropathy by regulating endoplasmic reticulum stress

Diabetic nephropathy (DN), a prevalent microvascular complication of diabetes mellitus, is the primary contributor to end-stage renal disease in developed countries. Existing clinical interventions for DN encompass lifestyle modifications, blood glucose regulation, blood pressure reduction, lipid management, and avoidance of nephrotoxic medications. Despite these measures, a significant number of patients progress to end-stage renal disease, underscoring the need for additional therapeutic strategies. The endoplasmic reticulum (ER) stress response, a cellular defense mechanism in eukaryotic cells, has been implicated in DN pathogenesis. Moderate ER stress can enhance cell survival, whereas severe or prolonged ER stress may trigger apoptosis. As such, the role of ER stress in DN presents a potential avenue for therapeutic modulation. Chinese herbal medicine, a staple in Chinese healthcare, has emerged as a promising intervention for DN. Existing research suggests that some herbal remedies may confer renoprotective benefits through the modulation of ER stress. This review explores the involvement of ER stress in the pathogenesis of DN and the advancements in Chinese herbal medicine for ER stress regulation, aiming to inspire new clinical strategies for the prevention and management of DN

The sialic acid-dependent nematocyst discharge process in relation to its physical-chemical properties is a role model for nanomedical diagnostic and therapeutic tools

Formulas derived from theoretical physics provide important insights about the nematocyst discharge process of Cnidaria (Hydra, jellyfishes, box-jellyfishes and sea-anemones). Our model description of the fastest process in living nature raises and answers questions related to the material properties of the cell- and tubule-walls of nematocysts including their polysialic acid (polySia) dependent target function. Since a number of tumor-cells, especially brain-tumor cells such as neuroblastoma tissues carry the polysaccharide chain polySia in similar concentration as fish eggs or fish skin, it makes sense to use these findings for new diagnostic and therapeutic approaches in the field of nanomedicine. Therefore, the nematocyst discharge process can be considered as a bionic blue-print for future nanomedical devices in cancer diagnostics and therapies. This approach is promising because the physical background of this process can be described in a sufficient way with formulas presented here. Additionally, we discuss biophysical and biochemical experiments which will allow us to define proper boundary conditions in order to support our theoretical model approach. PolySia glycans occur in a similar density on malignant tumor cells than on the cell surfaces of Cnidarian predators and preys. The knowledge of the polySia-dependent initiation of the nematocyst discharge process in an intact nematocyte is an essential prerequisite regarding the further development of target-directed nanomedical devices for diagnostic and therapeutic purposes. The theoretical description as well as the computationally and experimentally derived results about the biophysical and biochemical parameters can contribute to a proper design of anti-tumor drug ejecting vessels which use a stylet-tubule system. Especially, the role of nematogalectins is of interest because these bridging proteins contribute as well as special collagen fibers to the elastic band properties. The basic concepts of the nematocyst discharge process inside the tubule cell walls of nematocysts were studied in jellyfishes and in Hydra which are ideal model organisms. Hydra has already been chosen by Alan Turing in order to figure out how the chemical basis of morphogenesis can be described in a fundamental way. This encouraged us to discuss the action of nematocysts in relation to morphological aspects and material requirements. Using these insights, it is now possible to discuss natural and artificial nematocyst-like vessels with optimized properties for a diagnostic and therapeutic use, e.g., in neurooncology. We show here that crucial physical parameters such as pressure thresholds and elasticity properties during the nematocyst discharge process can be described in a consistent and satisfactory way with an impact on the construction of new nanomedical devices

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

ObjectivesTo explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.MethodsA total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.ResultsThe deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.ConclusionThis deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency