Baicalin Reduces Immune Cell Infiltration by Inhibiting Inflammation and Protecting Tight Junctions in Ischemic Stroke Injury.

Ischemic stroke is a serious health hazard that lacks effective treatment strategies. This study aims to investigate baicalin's effect on tight junctions and immune cell infiltration after ischemic stroke injury. Rat brain microvascular endothelial cells (BMECs) were treated with OGD/R to establish an in vitro model. Caspase-3, Bax, Bcl-2, zonula occludens-1 (ZO-1), occludin, claudin-5, tumor necrosis factor (TNF)-[Formula: see text], interleukin (IL)-6, inducible nitric oxide synthase (iNOS), Toll-like receptor (TLR) 2, TLR4, and nuclear factor-kappa B (NF-[Formula: see text]B) expressions were detected using qRT-PCR and western blotting. ZO-1, TNF-[Formula: see text], iNOS, IL6, CD31, and ZO-1 expressions were examined using immunofluorescence. A tube formation assay was performed to measure angiogenesis. An ischemia-reperfusion model in rats was established by middle cerebral artery occlusion. The infarct volume was observed using 2,3,5-triphenyltetrazolium chloride staining. TNF-[Formula: see text], iNOS, and IL6 levels in the serum were tested using ELISA. Flow cytometry was performed to examine immune cell inflammatory infiltration. Baicalin had no significant effect on the proliferation of normal BMECs. Baicalin inhibited apoptosis, protected against tight junction injury, and alleviated the inflammatory response in OGD/R-induced BMECs and IR rats, with the highest dose (25[Formula: see text][Formula: see text]g/mL) exerting a superior effect. Baicalin decreased the neurological function score, infarct volume, and brain water content, relieved brain morphological changes, and inhibited immune cell infiltration in vivo. In conclusion, baicalin could reduce BMECs apoptosis, protect tight junctions, and resist immune cell infiltration, thereby alleviating ischemic stroke. Our findings potentially provide a novel treatment strategy for ischemic stroke.

Promoting the Integration of Elderly Healthcare and Elderly Nursing: Evidence from the Chinese Government.

The increase of the aging population in China and the rise of the concept of healthy aging have accelerated the transformation and upgrading of the traditional elderly nursing pattern. Nevertheless, there is a critical limitation existing in the current situation of China's elderly care, i.e., the medical institutions do not support elderly nursing and the elderly nursing institutions do not facilitate access to medical care. To eliminate the adverse impact of this issue, twelve ministries and commissions of the Chinese government have jointly issued a document, i.e., the Several Opinions on Further Promoting the Development of Combining the Healthcare with the Elderly care (SOFPDCHE), to provide guidance from the government level for further promoting the integration of elderly healthcare and elderly nursing. Under this background, this paper constructs a healthcare-nursing information collaboration network (HnICN) based on the SOFPDCHE, proposing three novel strategies to explore the different roles and collaboration relationships of relevant government departments and public organizations in this integration process, i.e., the node identification strategy (NIS), the local adjacency subgroup strategy (LASS), and the information collaboration effect measurement strategy (ICEMS). Furthermore, this paper retrieves 484 valid policy documents related to "the integration of elderly healthcare and elderly nursing" as data samples on the official websites of 12 sponsored ministries and commissions, and finally confirms 22 government departments and public organizations as the network nodes based on these obtained documents, such as the National Health Commission of the People's Republic of China (NHC), the Ministry of Industry and Information Technology of the People's Republic of China (MIIT), and the National Working Commission on Aging (NWCA). In terms of the collaboration effect, the results of all node-pairs in the HnICN are significantly different, where the collaboration effect between the NHC and MIIT is best and that between the NATCM and MIIT is second best, which are 84.572% and 20.275%, respectively. This study provides the quantifiable results of the information collaboration degree between different government agencies and forms the optimization scheme for the current collaboration status based on these results, which play a positive role in integrating elderly healthcare and elderly nursing and eventually achieving healthy aging.

The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model.

The advent of the digital age has accelerated the transformation and upgrading of the traditional medical diagnosis pattern. With the rise of the concept of digital health, the emerging information technologies, such as machine learning (ML) and data mining (DM), have been extensively applied in the medical and health field, where the construction of disease prediction models is an especially effective method to realize auxiliary medical diagnosis. However, the existing related studies mostly focus on the prediction analysis for a certain disease, using models with which it might be challenging to predict other diseases effectively. To address the issues existing in the aforementioned studies, this paper constructs four novel strategies to achieve a self-adaptive disease prediction process, i.e., the hunger-state foraging strategy of producers (PHFS), the parallel strategy for exploration and exploitation (EEPS), the perturbation-exploration strategy (PES), and the parameter self-adaptive strategy (PSAS), and eventually proposes a self-adaptive disease prediction model with applied universality, strong generalization ability, and strong robustness, i.e., multi-strategies optimization-based kernel extreme learning machine (MsO-KELM). Meanwhile, this paper selects six different real-world disease datasets as the experimental samples, which include the Breast Cancer dataset (cancer), the Parkinson dataset (Parkinson's disease), the Autistic Spectrum Disorder Screening Data for Children dataset (Autism Spectrum Disorder), the Heart Disease dataset (heart disease), the Cleveland dataset (heart disease), and the Bupa dataset (liver disease). In terms of the prediction accuracy, the proposed MsO-KELM can obtain ACC values in analyzing these six diseases of 94.124%, 84.167%, 91.079%, 72.222%, 70.184%, and 70.476%, respectively. These ACC values have all been increased by nearly 2-7% compared with those obtained by the other models mentioned in this paper. This study deepens the connection between information technology and medical health by exploring the self-adaptive disease prediction model, which is an intuitive representation of digital health and could provide a scientific and reliable diagnostic basis for medical workers.

Alterations of gut microbiome and metabolite profiles in choledocholithiasis concurrent with cholangitis.

BACKGROUND AND AIMS: Gut microbiota and their metabolic products might play important roles in regulating the pathogenesis of choledocholithiasis concurrent with cholangitis (CC). The aim of this study was to explore the characteristic gut dysbiosis, metabolite profiles and the possible roles in patients with CC. METHODS: A case-control study was carried out to analyze the alterations in the intestinal microbiota and their metabolites in patients with CC (n = 25) compared with healthy controls (HCs) (n = 25) by metagenomic sequencing to define the gut microbiota community and liquid chromatography/mass spectrometry (LC/MS) analysis to characterize the metabolite profiles. RESULTS: Significantly reduced Shannon diversity index (p = 0.043) and differential overall fecal microbiota community in CCs were observed. Twelve dominant altered species were identified and analyzed (LDA score > 3.0, p < 0.05) (Q value < 0.05), including unclassified_f_Enterobacteriaceae, Escherichia_coli, Roseburia_faecis and Eubacterium rectale. Moreover, the levels of KEGG pathways related to biofilm formation of Escherichia coli, lipopolysaccharide (LPS) biosynthesis, and the metabolism of propanoate and glutathione in CCs were significantly altered. Finally, 47 markedly changed metabolites (VIP > 1.0 and p < 0.05), including low level of kynurenic acid (KYNA) and high concentration of N-palmitoylsphingosine involving tryptophan metabolism and sphingolipid signaling pathways, were identified to validate aberrant metabolic patterns in CCs, and multiple correlated metabolic modules involving bile inflammation were altered in CCs. CONCLUSION: Our study provides novel insights into compositional and functional alterations in the gut microbiome and metabolite profiles in CC and the underlying mechanisms between gut microbiota and bile inflammation.

Homology modeling and docking study of diamondback moth ryanodine receptor reveals the mechanisms for channel activation, insecticide binding and resistance.

BACKGROUND: Diamide insecticides, including phthalic and anthranilic diamides, target insect ryanodine receptors (RyRs) and cause misregulation of calcium signaling in insect muscles and neurons. Several resistance mutations have been reported to reduce the efficacy of the diamides, but the exact binding sites and mechanism of resistance mutations are not clear. RESULTS: The recent breakthrough in structural studies of mammalian RyRs has deepened our understanding of these giant calcium-release channels, but structural information about insect RyRs is still scarce. The only reported high-resolution structure is from the N-terminal domain of diamondback moth (DBM) RyR determined by our group. Here, we generate several homology models of full-length DBM RyR representing different functional states and dock the diamide insecticides into the structural models using Schrodinger software. These models reveal the specific structural features, activation mechanism, structural difference between functional states, ligand-binding sites and insecticide-binding sites of DBM RyR. By comparing the structures of wild-type and insecticide-resistant mutants, we propose a model depicting how the mutations affect the insecticide binding. We also identify the key difference between mammalian and insect RyRs that may explain the species-specific binding properties of diamides. CONCLUSION: The binding sites for three activators Ca2+ , ATP and caffeine, and regulator ryanodine are conserved in insect and mammalian RyRs, but the binding site for diamide insecticides is species-specific. The phthalic and anthranilic diamides have distinct binding properties in DBM, which can be interfered by resistance mutations located in the transmembrane region. © 2019 Society of Chemical Industry.

Pathogenic mechanism of a catecholaminergic polymorphic ventricular tachycardia causing-mutation in cardiac calcium release channel RyR2.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a condition that is characterized by an abnormal heart rhythm in response to physical or emotional stress. The majority CPVT patients carry mutations in the RYR2 gene that encodes the calcium release channel/ryanodine receptor (RyR2) in cardiomyocytes. The pathogenic mechanisms that account for the clinical phenotypes of CPVT are still elusive. We have identified a de novo mutation, A165D, from a CPVT patient. We found that CPVT phenotypes are recapitulated in A165D knock-in mice. The mutant RyR2 channels enhanced sarcoplasmic reticulum Ca2+ release, triggered delayed afterdepolarization in cardiomyocytes. Structural analysis revealed that the A165D mutation is located in a loop that is involved in inter-subunit interactions in the RyR2 tetrameric structure, it disrupted conformational stability of the RyR2, which favored a closed-to-open state transition, resulting in a leaky channel. The loop also harbors several other CPVT mutations, which suggests a common pathogenic molecular mechanism of CPVT-causing mutations. Our data illustrated disease-relevant functional defects and provide a deeper mechanistic understanding of a life-threatening cardiac arrhythmia.