Objective Iodothyronine deiodinases (DIOs) are important selenoproteins that play a key role in the bone and joint diseases. Osteoarthritis (OA) is the most prevalent joint disease especially in elders. This bioinformatic analysis was performed to explore the role of DIOs in OA pathogenesis. Methods The biological functions of selenoprotein DIOs were analyzed by bioinformatic techniques, including GenCLip 3.0, Database for Annotation, Visualization and Integrated Discovery (DAVID), STRING, Cytoscape, and Network Analyst. The expression of DIOs in the healthy individuals and OA patients was determined by mining OA-related microarray data in the gene expression omnibus (GEO) database of National Center for Biotechnology Information and performing a Meta-analysis of the data with Review Manager 5.3. Results Cluster analysis revealed that the function of the DIOs was associated with thyroid hormone receptor and iodothyronine; GO analysis showed that DIOs were mainly involved in biological processes, such as ethanol metabolism and phenol-containing compound metabolism and primarily involved in the cytochrome P450 metabolism of exogenous organisms and thyroid hormone signaling; SULT1A1 was the core node of the PPI network; miRNAs and thyroid hormones had some iterations with 

Biological Functions of Selenoprotein Iodothyronine Deiodinase and its Expression in Osteoarthritis.

Shenmai injection is frequently utilized in China to clinically treat granulocytopenia in oncology patients following chemotherapy. Despite this, the drug's therapeutic benefits remain a topic of contention, and its active components and potential treatment targets have yet to be established. The present study utilizes a network pharmacology approach to investigate the drug's active ingredients and possible therapeutic targets, and to evaluate the effectiveness of Shenmai injection in treating granulocytopenia through meta-analysis.
In our subject paper, we utilized the TCMID database to investigate the active ingredients present in red ginseng and ophiopogon japonicus. To further identify molecular targets, we employed SuperPred, as well as OMIM, Genecards, and DisGeNET databases. Our focus was on targets associated with granulocytopenia. The DAVID 6.8 database was utilized to perform gene ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Additionally, a protein-protein interaction network was established. The resulting "drug-key component-potential target-core pathway" network was used to predict the mechanism of action of Shenmai injection in the treatment of granulocytopenia. In order to evaluate the quality of the studies included in our analysis, we utilized the Cochrane Reviewers' Handbook. We then conducted a meta-analysis of the clinical curative effect of Shenmai injection for granulocytopenia, utilizing the Cochrane Collaboration's RevMan 5.3 software.
After conducting a thorough screening, the study identified 5 primary ingredients of Shenmai injection - ophiopogonoside a, β-patchoulene, ginsenoside rf, ginsenoside re, and ginsenoside rg1-that can potentially target 5 essential proteins: STAT3, TLR4, PIK3CA, PIK3R1, and GRB2. Additionally, Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that Shenmai injection can be beneficial in treating granulocytopenia by interacting with pathways such as HIF-1 signaling, T-cell receptor signaling, PI3K-Akt signaling, chemokine signaling, and FoxO signaling. The results of meta-analysis indicate that the treatment group exhibited superior performance in terms of both efficiency and post-treatment leukocyte count when compared to the control group.
In summary, studies in network pharmacology demonstrate that Shenmai injection exerts an impact on granulocytopenia via various components, targets, and mechanisms. Additionally, evidence-based studies provide strong support for the effectiveness of Shenmai injection in preventing and treating granulocytopenia.

Network analysis to explore the pharmacological mechanism of Shenmai injection in treating granulocytopenia and evidence-based medicine approach validation.

Strokes affect almost 13 million new people each year, and whereas the outcomes of stroke have improved over the past several decades in high-income countries, the same cannot be seen in low-income and lower-middle-income countries. This is the first study to identify the availability of diagnostic tools along with the rates of stroke mortality and other poststroke complications in low-income and lower-middle-income countries.
A review of the literature was completed with a search of the MEDLINE, Embase, and Scopus databases, with adherence to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were included if they reported any outcomes of stroke in low-income and lower-middle-income countries as designated by the World Bank classification. A meta-analysis calculating pooled prevalence rates of diagnostic characteristics and stroke outcomes was completed for all endpoint variables.
A total of 19 studies were included, of which 6 came from Ethiopia, 3 from Zambia, and 2 each from Tanzania and Iran. Single studies from Zimbabwe, Botswana, Senegal, Cameroon, Uganda, and Sierra Leone were included. A total of 5265 (61.7%) patients had an ischemic stroke, 2124 (24.9%) had hemorrhagic stroke, with the remaining 1146 (13.4%) having an unknown type. Among 6 studies the pooled percentage of patients presenting to hospital within 1 day was 48.37% (95% CI 38.59%-58.27%; I2 = 97.0%, p < 0.01). The pooled in-hospital mortality rate was 19.81% (95% CI 15.26%-25.31%; I2 = 91%, p < 0.01), but was higher in a hemorrhagic subgroup (27.07% [95% CI 22.52%-32.15%; I2 = 54%, p = 0.05]) when compared to an ischemic group (13.16% [95% CI 8.60%-19.62%; I2 = 87%, p < 0.01]). The 30-day pooled mortality rate was 23.24% (95% CI 14.17%-35.70%; I2 = 93%, p < 0.01). At 30 days, the functional independence (modified Rankin Scale score 0-2) pooled rate was 13.10% (95% CI 7.50%-21.89%; I2 = 82%, p < 0.01).
A severe healthcare disparity is present in low-income and lower-middle-income countries, where there is delayed diagnosis of strokes and increased rates of poor clinical outcomes for these patients.

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