DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases.

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.

Introduction: In recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research. Methods: Publications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies. Results and Discussion: In this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervision, balancing computing resources, and developing novel graph neural network models with better interpretability are the key technical issues currently faced. This paper analyzes the current state, hot spots, and trends of graph neural network applications in drug discovery through bibliometric approaches, as well as the current issues and challenges in this field. These findings provide researchers with valuable insights on the current status and future directions of this field.

Research focus and thematic trends of transient receptor potential vanilloid member 1 research: a bibliometric analysis of the global publications (1990-2023).

Recently, various studies have been devoted to the study of transient receptor potential vanilloid member 1 (TRPV1)-related diseases, potential drugs, and related mechanisms. The objective of this investigation was to examine the significant areas and cutting-edge developments in TRPV1 study within recent decades. Articles or reviews were obtained from the Web of Science Core Collection. VOSviewer 1.6.18 and CiteSpace 6.1 R2 software were utilized to examine publication growth, distribution by country/region, institution, journal, authorship, references, and keywords. The software identified keywords with a high citation burstiness to determine emerging topics. From 1990 to 2023, the annual global publications increased by 62,000%, from 1 to 621. Journal of neuroscience published the most manuscripts and Nature produced the highest citations. The USA, Seoul National University and Di marzo V were the most productive and impactful institution, country, and author, respectively. "TRPV1," "Capsaicin receptor," "Activation," and "Pain" are the most important keywords. The burst keywords "TRPV1 channel," "Oxidative stress," "TRPV1 structure," and "Cancer" are supposed to be the research frontiers. The present study offers valuable insights into the understanding of TRPV1 and pain-related conditions. The research on TRPV1 has demonstrated a steady increase in studies related to pain-related diseases in the past few decades. The significance of TRPV1 in cancer pathogenesis and the resolution of its structure will emerge as a new academic trend in this field, providing direction for more widespread and comprehensive studies in the future.

Butein, a potential drug for the treatment of bone cancer pain through bioinformatic and network pharmacology.

Bone cancer pain is a difficult-to-treat pathologic condition that impairs the patient's quality of life. The effective therapy options for BCP are restricted due to the unknown pathophysiology. Transcriptome data were obtained from the Gene Expression Omnibus database and differentially expressed gene extraction was performed. DEGs integrated with pathological targets found 68 genes in the study. Butein was discovered as a possible medication for BCP after the 68 genes were submitted to the Connectivity Map 2.0 database for drug prediction. Moreover, butein has good drug-likeness properties. To collect the butein targets, we used the CTD, SEA, TargetNet, and Super-PRED databases. Furthermore, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed butein's pharmacological effects, indicating that butein may aid in treating BCP by altering the hypoxia-inducible factor, NF-kappa B, angiogenesis, and sphingolipid signaling pathways. Moreover, the pathological targets integrated with drug targets were obtained as the shared gene set A, which was analyzed by ClueGO and MCODE. Biological process analysis and MCODE algorithm further analyzed that BCP related targets were mainly involved in signal transduction process and ion channel-related pathways. Next, we integrated targets related to network topology parameters and targets of core pathways, identified PTGS2, EGFR, JUN, ESR1, TRPV1, AKT1 and VEGFA as butein regulated hub genes by molecular docking, which play a critical role in its analgesic effect. This study lays the scientific groundwork for elucidating the mechanism underlying butein's success in the treatment of BCP.

Develop adult extrapolation to pediatrics and pediatric dose optimization based on the physiological pharmacokinetic model of azithromycin.

Physiologically-based pharmacokinetic (PBPK) models are more frequently used for supporting pediatric dose selection in small-molecule drugs. Through literature research, drug parameters of azithromycin and clinical data from different studies were obtained. Through parameter optimization of the absorption and dissolution process, the adult intravenous model was extended to the adult oral model. The adult intravenous and oral PBPK models are precise to meet the AAFE<2 standard, and the pharmacokinetic parameters of the predicted values of the model are all within the mean standard deviation of the clinical observations. The values of plasma protein unbound fraction, renal clearance, and gastric juice pH between adults and pediatrics were changed by using the age-dependent pediatric organ maturity formula, and the adult model was extrapolated to the pediatric model. The final developed pediatric PBPK model was used to evaluate optimal dosing for children of different developmental ages. The relationship between the frist dose and age was as follows: 8.8 mg/kg/day from 0.5 to 2 years old, 9.2 mg/kg/day from 3 to 6 years old, 9.4 mg/kg/day from 7 to 12 years old, and 8.2 mg/kg/day from 13 to 18 years old, taken in half for 2-5 days. Simultaneously, the simulated exposures achieved with the dosing regimen proposed were comparable to adult plasma exposures for treatment of community-acquired pneumonia. A reasonable azithromycin pharmacokinetic-pharmacodynamic model for adults and pediatrics has been established, which can be demonstrated by the use of literature pediatric data to develop pediatric PBPK models, expanding the scope of this powerful modeling tool.

Ensemble learning-based gene signature and risk model for predicting prognosis of triple-negative breast cancer.

Although medical science has been fully developed, due to the high heterogeneity of triple-negative breast cancer (TNBC), it is still difficult to use reasonable and precise treatment. In this study, based on local optimization-feature screening and genomics screening strategy, we screened 25 feature genes. In multiple machine learning algorithms, feature genes have excellent discriminative diagnostic performance among samples composed of multiple large datasets. After screening at the single-cell level, we identified genes expressed substantially in myeloid cells (MCGs) that have a potential association with TNBC. Based on MCGs, we distinguished two types of TNBC patients who showed considerable differences in survival status and immune-related characteristics. Immune-related gene risk scores (IRGRS) were established, and their validity was verified using validation cohorts. A total of 25 feature genes were obtained, among which CXCL9, CXCL10, CCL7, SPHK1, and TREM1 were identified as the result after single-cell level analysis and screening. According to these entries, the cohort was divided into MCA and MCB subtypes, and the two subtypes had significant differences in survival status and tumor-immune microenvironment. After Lasso-Cox screening, IDO1, GNLY, IRF1, CTLA4, and CXCR6 were selected for constructing IRGRS. There were significant differences in drug sensitivity and immunotherapy sensitivity among high-IRGRS and low-IRGRS groups. We revealed the dynamic relationship between TNBC and TIME, identified a potential biomarker called Granulysin (GNLY) related to immunity, and developed a multi-process machine learning package called "MPMLearning 1.0" in Python.

Ionizable drug delivery systems for efficient and selective gene therapy.

Gene therapy has shown great potential to treat various diseases by repairing the abnormal gene function. However, a great challenge in bringing the nucleic acid formulations to the market is the safe and effective delivery to the specific tissues and cells. To be excited, the development of ionizable drug delivery systems (IDDSs) has promoted a great breakthrough as evidenced by the approval of the BNT162b2 vaccine for prevention of coronavirus disease 2019 (COVID-19) in 2021. Compared with conventional cationic gene vectors, IDDSs can decrease the toxicity of carriers to cell membranes, and increase cellular uptake and endosomal escape of nucleic acids by their unique pH-responsive structures. Despite the progress, there remain necessary requirements for designing more efficient IDDSs for precise gene therapy. Herein, we systematically classify the IDDSs and summarize the characteristics and advantages of IDDSs in order to explore the underlying design mechanisms. The delivery mechanisms and therapeutic applications of IDDSs are comprehensively reviewed for the delivery of pDNA and four kinds of RNA. In particular, organ selecting considerations and high-throughput screening are highlighted to explore efficiently multifunctional ionizable nanomaterials with superior gene delivery capacity. We anticipate providing references for researchers to rationally design more efficient and accurate targeted gene delivery systems in the future, and indicate ideas for developing next generation gene vectors.

A bibliometric and visualized analysis of liver fibrosis from 2002 to 2022.

Fibrosis of the liver is a degenerative alteration that occurs in the majority of chronic liver disorders. Further progression can lead to cirrhosis, liver failure, and hepatocellular carcinoma, which can seriously affect the health and lives of patients. The field of liver fibrosis research has flourished in the last 20 years, with approximately 9000 articles retrieved from the Web of Science Core Collection database alone. In order to identify future research hotspots and potential paths in a thorough and scientifically reliable manner, it is important to organize and visualize the research on this topic from a holistic and very general perspective. This study used bibliometric analysis with CiteSpace and VOSviewer software to provide a quantitative analysis, hotspot mining, and commentary of articles published in the field of liver fibrosis over the last 20 years. This bibliometric analysis contains a total of 8994 articles with 45667 authors from 6872 institutions in 97 countries, published in 1371 journals and citing 156 309 references. The literature volume has steadily increased over the last 20 years. Research has focused on gastroenterology and hepatology, pharmacology and pharmacy, and medicine, research, and experimental areas. We found that the pathological mechanisms, diagnostic and quantitative methods, etiology, and antifibrotic strategies constitute the knowledge structure of liver fibrosis. Finding mechanisms for liver fibrosis regression, identifying precise noninvasive diagnostic and prognostic biomarkers, and creating efficient liver fibrosis patient treatments are the main goals of current research.

Artificial intelligence-driven prediction of multiple drug interactions.

When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug-drug, drug-food (excipients) and drug-microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug-drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.

An optimized herbal combination for the treatment of liver fibrosis: Hub genes, bioactive ingredients, and molecular mechanisms.

ETHNOPHARMACOLOGICAL RELEVANCE: Liver fibrosis is a chronic liver disease that can lead to cirrhosis, liver failure, and hepatocellular carcinoma, and it is associated with long-term adverse outcomes and mortality. As a primary resource for complementary and alternative medicine, traditional Chinese medicine (TCM) has accumulated a large number of effective formulas for the treatment of liver fibrosis in clinical practice. However, studies on how to systematically optimize TCM formulas are still lacking. AIM OF THE REVIEW: To provide a methodological reference for the systematic optimization of TCM formulae against liver fibrosis and explored the underlying molecular mechanisms; To provide an efficient method for searching for lead compounds from natural sources and developing from herbal medicines; To enable clinicians and patients to make more reasonable choices and promote the effective treatment toward those patients with liver fibrosis. MATERIALS AND METHODS: TCM formulas related to treating liver fibrosis were collected from the Web of Science, PubMed, the China National Knowledge Infrastructure (CNKI), Wan Fang, and the Chinese Scientific Journals Database (VIP). Furthermore, the TCM compatibility patterns were mined using association analysis. The core TCM combinations were found by designing an optimized formulas algorithm. Finally, the hub target proteins, potential molecular mechanisms, and active compounds were explored through integrative pharmacology and docking-based inverse virtual screening (IVS) approaches. RESULTS: We found that the herbs for reinforcing deficiency, activating blood, removing blood stasis, and clearing heat were the basis of TCM formulae patterns. Furthermore, the combination of Salviae Miltiorrhizae (Salvia miltiorrhiza Bunge; Chinese salvia/Danshen), Astragali Radix (Astragalus membranaceus (Fisch.) Bunge; Astragalus/Huangqi), and Radix Bupleuri (Bupleurum chinense DC.; Bupleurum/Chaihu) was identified as core groups. A total of six targets (TNF, STAT3, EGFR, IL2, ICAM1, PTGS2) play a pivotal role in TCM-mediated liver fibrosis inhibition. (-)-Cryptotanshinone, Tanshinaldehyde, Ononin, Thymol, Daidzein, and Formononetin were identified as active compounds in TCM. And mechanistically, TCM could affect the development of liver fibrosis by regulating inflammation, immunity, angiogenesis, antioxidants, and involvement in TNF, MicroRNAs, Jak-STAT, NF-kappa B, and C-type lectin receptors (CLRs) signaling pathways. Molecular docking results showed that key components had good potential to bind to the target genes. CONCLUSION: In summary, this study provides a methodological reference for the systematic optimization of TCM formulae and exploration of underlying molecular mechanisms.

Analysis of CFTR gene expression as an immunological and prognostic biomarker in pan-cancers.

Cystic fibrosis transmembrane conductance regulator (CFTR) is a cAMP-activated chloride channel that regulates fluid homeostasis via ATP binding and uses energy to transport relevant substrates across cytomembranes. It has been reported that CFTR plays a crucial role in the incidence and development of various types of cancers by regulating proliferation, metastasis, invasion and apoptosis. However, aberrant CFTR gene expression across different cancers makes it difficult to propose CFTR as a possible pan-cancer biomarker. Here, multiple databases (ONCOMINE, PrognoScan, Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA)), were accessed to investigate the relationship between CFTR gene expression with the immunological and prognostic roles in pan-cancers. The results showed higher CFTR gene expression in tumor tissues compared to normal tissues for most cancers except for CHOL, ESCA, KICH, LAML, SKCM and STAD. Higher expression of the CFTR gene directly correlated with better prognosis for BRCA, GBM, COAD, KIRP, LAML, LUAD, PRAD, SARC and STAD, and CFTR gene expression was higher in stage Ⅰ_Ⅱ compared to stage Ⅲ_ Ⅳ. Furthermore, CFTR gene expression levels were significantly associated with immune infiltrates and immunocytes, in particular, immune checkpoints, in COAD, LIHC, LUAD and LUSC. In conclusion, CFTR can be used as a prognostic marker for nine types of cancers examined in this study where CFTR expression levels play a vital role in forecasting the clinical efficacy of immune checkpoint suppression therapy.

Systematic analysis of the mechanism of Xiaochaihu decoction in hepatitis B treatment via network pharmacology and molecular docking.

Hepatitis B (HB) is a globally prevalent infectious disease caused by the HB virus. Xiaochaihu decoction (XCHD) is a classic herbal formula with a long history of clinical application in treating HB. Although the anti-HB activity of XCHD has been reported, systematic research on the exact mechanism of action is lacking. Here, a network pharmacology-based approach was used to predict the active components, important targets, and potential mechanism of XCHD in HB treatment. Investigation included drug-likeness evaluation; absorption, distribution, metabolism, and elimination (ADME) screening; protein-protein interaction (PPI) network construction and cluster analysis; Gene Ontology (GO) analysis; and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation. Molecular docking was adopted to investigate the interaction between important target proteins and active components. Eighty-seven active components of XCHD and 155 anti-HB targets were selected for further analysis. The GO enrichment and similarity analysis results indicated that XCHD might perform similar or the same GO functions. Glycyrrhizae Radix (GR), one of the seven XCHD herbs, likely exerts some unique GO functions such as the regulation of interleukin-12 production, positive regulation of interleukin-1 beta secretion, and regulation of the I-kappaB/NF-kappaB complex. The PPI network and KEGG pathway analysis results showed that XCHD affects HB mainly through modulating pathways related to viral infection, immunity, cancer, signal transduction, and metabolism. Additionally, molecular docking verified that the active compounds (quercetin, chrysin, and capsaicin) could bind with the key targets. This work systematically explored the anti-HB mechanism of XCHD and provides a novel perspective for future pharmacological research.

Incretin mimetics and sodium-glucose co-transporter 2 inhibitors as monotherapy or add-on to metformin for treatment of type 2 diabetes: a systematic review and network meta-analysis.

PURPOSE: Although there are many different methods of treating type 2 diabetes (T2D), it is still difficult to draw coincident conclusions concerning the efficacy and safety of different classes of new drugs, and the recommendation level of them has still kept uncertain as second anti-diabetic agents. Therefore, the aim of this study was to summarize evidence on the efficacy and safety of DPP-4is, GLP-1RAs and SGLT-2is as monotherapy or add-on to metformin (Met) for treatment of T2D. MATERIALS AND METHODS: We searched PubMed, Embase, Cochrane library and ClinicalTrials.gov for relevant articles in keeping with established methods using terms associated with anti-diabetic agents up to February, 2020, with no start date restriction. Weighted mean difference and risk ratios with 95% confidence intervals were calculated within traditional and network meta-analysis. Primary outcomes were the mean change in hemoglobin A1c (HbA1c), fasting plasma glucose (FPG) change and the frequency of hypoglycemic events from baseline after 12 weeks of treatment. RESULTS: In total, 64 eligible studies comprising 37,780 patients and 7 treatment strategies were included. The results of primary outcomes showed that GLP-1RAs were significantly more effective than DPP-4is or SGLT-2is in reducing HbA1c when add-on to Met. For FPG, both GLP-1RAs and SGLT-2is significantly reduced FPG compared with DPP-4is whether add-on to Met or not. For hypoglycemia, monotherapy has a lower risk than combination therapy except for SGLT-2is. Ranking probability analysis indicated that GLP-1RAs and SGLT-2is, respectively, reduced HbA1c and FPG most when add-on to Met. Meanwhile, GLP-1RAs took the lowest risk to induce the hypoglycemia, whereas GLP-1RAs plus Met the highest. CONCLUSIONS: Both GLP-1RAs and SGLT-2is have their own advantages in efficacy and safety. Monotherapy is beneficial for reducing the risk of hypoglycemia. The recommendation should be a patient-centered approach when selecting treatment choices.

Genomic characterization of IncpA1763-KPC: IncFIIK7 type plasmids p13294-KPC and pA1966-NR from Klebsiella pneumoniae.

Aim: To characterize two plasmids p13294-KPC and pA1966-NR from clinical Klebsiella pneumoniae strains. Materials & methods: Plasmids p13294-KPC and pA1966-NR were fully sequenced and then detailed genomic analysis was performed in this work. The antimicrobial resistance phenotypes were determined. Results: p13294-KPC and pA1966-NR displayed IncpA1763-KPC:IncFIIK7 dual-replicon structures. The backbone of these two plasmids were closely related to each other. p13294-KPC contained two accessory modules, namely ΔISKpn25 and blaKPC-2 region, and the blaKPC-2 region carried a range of mobile elements and resistance gene blaKPC-2. while pA1966-NR contained four individual IS elements in its backbone and carried no resistance genes. Conclusion: This study provided a deeper insight into the genomic characterization of IncpA1763-KPC: IncFIIK7 type plasmids p13294-KPC and pA1966-NR.

Research Progress of Coronavirus Based on Bibliometric Analysis.

BACKGROUND: COVID-19 has become one of the most serious global epidemics in the 21st Century. This study aims to explore the distribution of research capabilities of countries, institutions, and researchers, and the hotspots and frontiers of coronavirus research in the past two decades. In it, references for funding support of urgent projects and international cooperation among research institutions are provided. METHOD: the Web of Science core collection database was used to retrieve the documents related to coronavirus published from 2003 to 2020. Citespace.5.6.R2, VOSviewer1.6.12, and Excel 2016 were used for bibliometric analysis. RESULTS: 11,036 documents were retrieved, of which China and the United States have contributed the most coronavirus studies, Hong Kong University being the top contributor. Regarding journals, the JournalofVirology has contributed the most, while in terms of researchers, Yuen Kwok Yung has made the most contributions. The proportion of documents published by international cooperation has been rising for decades. Vaccines for SARS-CoV-2 are under development, and clinical trials of several drugs are ongoing. CONCLUSIONS: international cooperation is an important way to accelerate research progress and achieve success. Developing corresponding vaccines and drugs are the current hotspots and research directions.

N2 neutrophils may participate in spontaneous recovery after transient cerebral ischemia by inhibiting ischemic neuron injury in rats.

Neutrophils have been traditionally considered as the major mediators of harmful inflammatory responses in ischemic stroke, whereas accumulating evidence indicates that neutrophils can be polarized into an N2 phenotype. Similar to M2 microglia, N2 neutrophils contribute to resolution of inflammation and may participate in neuroprotection. However, it remains unclear whether N2 neutrophils protect ischemic neurons and whether they are associated with long-term outcomes after transient cerebral ischemia in rats. The present study proved that N2 neutrophils protected against oxygen glucosedeprivation/re-oxygenation (OGD/R)-induced primary cortical neuron injury via brain-derived neurotrophic factor/tropomyosin-related kinase B (BDNF/TrkB) signaling. In addition, in vivo studies revealed that transient middle cerebral artery occlusion (tMCAO)-induced injury exhibited spontaneous recovery over time in rats. Moreover, neutrophils could infiltrate the ipsilateral brain parenchyma from the periphery after transient cerebral ischemia. Pearson's correlation analysis indicated that the proportion of N2 neutrophils in ipsilateral brain parenchyma was negatively correlated with the number of degenerating neurons, modified Neurological Severity Score (mNSS), brain water content and infarct volume, and positively correlated with the number of surviving neurons and grip strength. In summary, the present study shows that N2 neutrophils likely participate in spontaneous recovery after transient cerebral ischemia by inhibiting ischemic neuron damage in rats, which indicates that N2 neutrophils may represent promising therapeutic target for promoting recovery after ischemic stroke.

Pseudoginsenoside-F11 Attenuates Lipopolysaccharide-Induced Acute Lung Injury by Suppressing Neutrophil Infiltration and Accelerating Neutrophil Clearance.

Pseudoginsenoside-F11 (PF11), an ocotillol-type saponin, has been reported to have anti-inflammatory properties, but the effects of PF11 on acute lung inflammation were unknown. The present study aimed to investigate the protective effects and potential mechanisms of PF11 on lipopolysaccharide (LPS)-induced acute lung injury (ALI) in male BALB/c mice. After being treated with PF11 (3, 10, and 30 mg/kg, intravenous) once a day for 3 consecutive days, the mice were challenged by intratracheal instillation of LPS, and then their lung tissues and bronchoalveolar lavage fluid (BALF) were collected for further analysis. The results showed that PF11 attenuated LPS-induced ALI, with alleviated histopathological damage, decreased lung wet/dry weight ratio, and reduced protein concentration and inflammatory cells number in BALF. Moreover, PF11 reversed the LPS-induced increases of mRNA expression and protein levels of interleukin-6, tumor necrosis factor-α, and interleukin-1ß. Meanwhile, PF11 decreased LPS-induced myeloperoxidase activity and neutrophil infiltration in lung tissue by reducing the expression of macrophage inflammatory protein-2 and intercellular adhesion molecule-1, as well as enhanced neutrophil clearance by accelerating neutrophils apoptosis and their phagocytosis by alveolar macrophages. In conclusion, these results indicated that PF11 significantly attenuated LPS-induced ALI through suppressing neutrophil infiltration and accelerating neutrophil clearance, suggesting its potential in the treatment of ALI.

Comparative efficacy and safety of multiple antiplatelet therapies for secondary prevention of ischemic stroke or transient ischemic attack: A network meta-analysis.

BACKGROUND: Antiplatelet therapies for secondary prevention of ischemic stroke or transient ischemic attack (TIA) is a highly active research topic with five critical drugs obtained by visual analysis. We aimed to compare and rank multiple antiplatelet therapies using a network meta-analysis. METHODS: Relevant medical databases were searched. Eligible randomized controlled trials (RCTs) which examined any comparisons involving mono- or dual antiplatelet therapies, based on aspirin, clopidogrel, dipyridamole, ticlopidine, cilostazol and placebo for patients with noncardioembolic ischemic stroke or TIA, were included. 14 outcomes were assessed. Primary outcomes were stroke recurrence, composite events (stroke recurrence, myocardial infarction and vascular death), and intracranial hemorrhage. PROSPERO registered number CRD42017069728. RESULTS: 45 RCTs with 173,131 patients were included in network meta-analysis, involving eight antiplatelet therapies. Cilostazol and clopidogrel were statistically more efficacious than aspirin (odds ratio (OR) = 0.64, 95% confidence interval (CI) = 0.47-0.88; OR = 0.77, 95%CI = 0.62-0.95) and dipyridamole (OR = 0.64, 95%CI = 0.44-0.93; OR = 0.76, 95%CI = 0.58-0.99) in reducing stroke recurrence, and showed significant benefits in reducing composite events compared with aspirin (OR = 0.63, 95%CI = 0.45-0.89; OR = 0.90, 95%CI = 0.83-0.97). No significant difference was found between cilostazol and clopidogrel in intracranial hemorrhage. Weighted regression suggested cilostazol was hierarchically the optimum treatment in consideration of both efficacy and safety, followed by clopidogrel. CONCLUSION: Cilostazol and clopidogrel are probably promising options for secondary prevention of ischemic stroke or TIA. Both of them reduce stroke recurrence similarly compared with aspirin or dipyridamole, and reduce composite events compared with aspirin. Further studies are needed to confirm this finding.

Efficacy and safety of sodium-glucose cotransporter-2 inhibitors versus dipeptidyl peptidase-4 inhibitors as monotherapy or add-on to metformin in patients with type 2 diabetes mellitus: A systematic review and meta-analysis.

AIMS: To compare the efficacy and safety of dipeptidyl peptidase-4 inhibitors (DPP-4is) and sodium-glucose cotransporter-2 inhibitors (SGLT-2is) as monotherapy or add-on to metformin (Met) in patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: PubMed, Embase and ClinicalTrials.gov sites were systematically searched for randomized controlled trials to assess the efficacy and safety of DPP-4is and SGLT-2is in patients with T2DM. Risk ratio (RR) and weighted mean difference (WMD) were used to evaluate outcomes. RESULTS: In the analysis of 25 randomized trials, which involved 14 619 patients, SGLT-2is were associated with a significantly stronger reduction in haemoglobin A1c (HbA1c) (WMD 0.13%, 95% credible interval [CI], 0.04%-0.22%, P = .005) and fasting plasma glucose (FPG) (WMD 0.80 mmol/L, 95% CI, 0.58-1.01 mmol/L, P < .00001) than were DPP-4is. However, no significant difference between the 2 drug categories was found in the risk of hypoglycaemic events (RR, 0.99; 95% CI, 0.78-1.26, P = .92). SGLT-2is plus Met was associated with a more significant decrease in FPG (WMD 0.71 mmol/L, 95% CI, 0.43-1.00 mmol/L, P < .00001) than was DPP-4is plus Met. However, no differences were found in the reduction of HbA1c (WMD 0.11%, 95% CI, -0.03%-0.25%, P = .12) or the risk of hypoglycaemic events (RR, 1.02; 95% CI, 0.80-1.31, P = .86). CONCLUSIONS: This review revealed that, compared to DPP-4is, SGLT-2is significantly reduced HbA1c, FPG and body weight without increasing the risk of hypoglycaemia in diabetes treatment.