SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.

Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate manual annotations significantly hinders the performance of machine learning algorithms. In this study, a new DDI prediction framework, Subgraph Enhance model, was developed for DDI (SubGE-DDI) to improve the performance of machine learning algorithms. This model uses drug pairs knowledge subgraph information to achieve large-scale plain text prediction without many annotations. This model treats DDI prediction as a multi-class classification problem and predicts the specific DDI type for each drug pair (e.g. Mechanism, Effect, Advise, Interact and Negative). The drug pairs knowledge subgraph was derived from a huge drug knowledge graph containing various public datasets, such as DrugBank, TwoSIDES, OffSIDES, DrugCentral, EntrezeGene, SMPDB (The Small Molecule Pathway Database), CTD (The Comparative Toxicogenomics Database) and SIDER. The SubGE-DDI was evaluated from the public dataset (SemEval-2013 Task 9 dataset) and then compared with other state-of-the-art baselines. SubGE-DDI achieves 83.91% micro F1 score and 84.75% macro F1 score in the test dataset, outperforming the other state-of-the-art baselines. These findings show that the proposed drug pairs knowledge subgraph-assisted model can effectively improve the prediction performance of DDIs from biomedical texts.

A real-world disproportionality analysis of anti-VEGF drugs from the FDA Adverse Event Reporting System.

BACKGROUND: The association between anti-vascular endothelial growth factor (VEGF) drugs and ocular adverse events (AEs) has been reported, but large real-world studies of their association with systemic AEs are still lacking. METHODS: A disproportionality analysis of reports from the FDA Adverse Event Reporting System from January 2004 to September 2021 was conducted to detect the significant ADR signals with anti-VEGF drugs (including aflibercept, bevacizumab, brolucizumab, pegaptanib, and ranibizumab). RESULTS: A total of 2980 reported cases with 7125 drug-AEs were included. Five drugs were all associated with eye disorders, and pegaptanib and ranibizumab were also associated with cardiac disorders. For ranibizumab, pegaptanib, bevacizumab and aflibercept, the proportions of cardiac AEs were 8.57%, 5.62%, 3.43% and 3.20%, respectively, and the proportions of central nervous AEs were 8.81%, 7.41, 5.86% and 5.68%, respectively. In multiple comparisons, ranibizumab was significantly higher than bevacizumab and aflibercept in the proportion of cardiac AEs (P < 0.001), and ranibizumab was significantly higher than aflibercept in central nervous AEs (P < 0.001). CONCLUSIONS: Our findings support the associations between anti-VEGF drugs and ocular AEs, cardiac AEs, and central nervous AEs. After intravitreal injection, attention should not only be paid to ocular symptoms, but also to systemic symptoms.

Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma.

BACKGROUND: Memory B cells and microRNAs (miRNAs) play important roles in the progression of gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD). However, few studies have investigated the use of memory B-cell-associated miRNAs in predicting the prognosis of STAD. METHODS: We identified the marker genes of memory B cells by single-cell RNA sequencing (scRNA-seq) and identified the miRNAs associated with memory B cells by constructing an mRNA‒miRNA coexpression network. Then, univariate Cox, random survival forest (RSF), and stepwise multiple Cox regression (StepCox) algorithms were used to identify memory B-cell-associated miRNAs that were significantly related to overall survival (OS). A prognostic risk model was constructed and validated using these miRNAs, and patients were divided into a low-risk group and a high-risk group. In addition, the differences in clinicopathological features, tumour microenvironment, immune blocking therapy, and sensitivity to anticancer drugs in the two groups were analysed. RESULTS: Four memory B-cell-associated miRNAs (hsa-mir-145, hsa-mir-125b-2, hsa-mir-100, hsa-mir-221) with significant correlations to OS were identified and used to construct a prognostic model. Time-dependent receiver operating characteristic (ROC) curve analysis confirmed the feasibility of the model. Kaplan‒Meier (K‒M) survival curve analysis showed that the prognosis was poor in the high-risk group. Comprehensive analysis showed that patients in the high-risk group had higher immune scores, matrix scores, and immune cell infiltration and a poor immune response. In terms of drug screening, we predicted eight drugs with higher sensitivity in the high-risk group, of which CGP-60474 was associated with the greatest sensitivity. CONCLUSIONS: In summary, we identified memory B-cell-associated miRNA prognostic features and constructed a novel risk model for STAD based on scRNA-seq data and bulk RNA-seq data. Among patients in the high-risk group, STAD showed the highest sensitivity to CGP-60474. This study provides prognostic insights into individualized and precise treatment for STAD patients.

Graph-DTI: A New Model for Drug-Target Interaction Prediction Based on Heterogenous Network Graph Embedding.

BACKGROUND: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score. METHOD: Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification. RESULTS: The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results. CONCLUSION: Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph-DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.

Gender Differences in the Nonspecific and Health-Specific Use of Social Media Before and During the COVID-19 Pandemic: Trend Analysis Using HINTS 2017-2020 Data.

The use of social media has changed since the outbreak of coronavirus disease 2019 (COVID-19). However, little is known about the gender disparity in social media use for nonspecific and health-specific issues before and during the COVID-19 pandemic. Based on a gender difference perspective, this study aimed to examine how the nonspecific and health-specific uses of social media changed in 2017-2020. The data came from the Health Information National Trends Survey Wave 5 Cycle 1-4. This study included 10,426 participants with complete data. Compared to 2017, there were higher levels of general use in 2019 and 2020, and an increased likelihood of health-related use in 2020 was reported among the general population. Female participants were more likely to be nonspecific and health-specific users than males. Moreover, the relationship of gender with general use increased in 2019 and 2020; however, concerning health-related use, it expanded in 2019 but narrowed in 2020. The COVID-19 global pandemic led to increased use of social media, especially for health-related issues among males. These findings further our understanding of the gender gap in health communication through social media, and contribute to targeted messaging to promote health and reduce disparities between different groups during the pandemic.

SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction.

BACKGROUND: The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS: In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS: The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION: The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.

Systematic evaluation of the prognostic and immunological role of PDLIM2 across 33 cancer types.

The protein PDLIM2 regulates the stability of various transcription factors and is required for polarized cell migration. However, the clinical relevance and immune infiltration of PDLIM2 in cancer are not well-understood. We utilized The Cancer Genome Atlas and Genotype-Tissue Expression database to characterize alterations in PDLIM2 in pan-cancer. TIMER was used to explore PDLIM2 expression and immune infiltration levels. We assessed the correlation between PDLIM2 expression and immune-associated gene expression, immune score, tumor mutation burden, and DNA microsatellite instability. PDLIM2 significantly affected the prognosis of various cancers. Increased expression of PDLIM2 was significantly correlated with the tumor grade in seven types of tumors. The expression level of PDLIM2 was positively correlated with immune infiltrates, including B cells, CD8+ T cells, CD4+ T cells, neutrophils, macrophages, and dendritic cells in bladder urothelial, kidney renal papillary cell, and colon adenocarcinoma. High expression levels of PDLIM2 tended to be associated with higher immune and stromal scores. PDLIM2 expression was associated with the tumor mutation burden in 12 cancer types and microsatellite instability in 5 cancer types. PDLIM2 levels were strongly correlated with diverse immune-related genes. PDLIM2 can act as a prognostic-related therapeutic target and is correlated with immune infiltrates in pan-cancer.

Prognostic and Immunological Roles of MMP-9 in Pan-Cancer.

BACKGROUND: Matrix metalloproteinase-9 (MMP-9) can degrade the extracellular matrix and participate in tumor progression. The relationship between MMP-9 and immune cells has been reported in various malignant tumors. However, there is a lack of comprehensive pan-cancer studies on the relationship between MMP-9 and cancer prognosis and immune infiltration. METHOD: We used data from TCGA and GTEx databases to comprehensively analyze the differential expression of MMP-9 in normal and cancerous tissues. Survival analysis was performed to understand the prognostic role of MMP-9 in different tumors. We then analyzed the expression of MMP-9 across different tumors and at different clinical stages. Based on the results, we assessed the correlation between MMP-9 expression and immune-associated genes and immunocytes. Finally, we calculated the tumor mutation burden (TMB) of 33 cancer types and analyzed the correlation between MMP-9 and TMB, DNA microsatellite instability, and DNA repair genes. RESULTS: MMP-9 significantly affected the prognosis and metastasis of various cancers. It was associated based on overall survival, disease-specific survival in five tumors, progression-free interval in seven tumors, and clinical stage in eight tumors, as well as with prognosis and metastasis in adrenocortical carcinoma and kidney renal clear cell carcinoma. It was also coexpressed with immune-related genes and DNA repair genes. The expression of MMP-9 was positively correlated with the markers of T cells, tumor-associated macrophages, Th1 cells, and T cell exhaustion. Furthermore, MMP-9 expression was highly correlated with macrophage M0 in 28 tumors. In addition, its expression was associated with TMB in eight cancer types and DNA microsatellite instability in six cancer types. CONCLUSION: MMP-9 is related to immune infiltration in pan-cancer and can be used as a biomarker related to cancer prognosis and metastasis. Our findings provide prognostic molecular markers and new ideas for immunotherapy.

An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications.

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well-known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms.

Study on the Mechanism of Liuwei Dihuang Pills in Treating Parkinson's Disease Based on Network Pharmacology.

BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease in middle-aged and elderly people. Liuwei Dihuang (LWDH) pills have a good effect on PD, but its mechanism remains unclear. Network pharmacology is the result of integrating basic theories and research methods of medicine, biology, computer science, bioinformatics, and other disciplines, which can systematically and comprehensively reflect the mechanism of drug intervention in disease networks. METHODS: The main components and targets of herbs in LWDH pills were obtained through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Its active components were screened based on absorption, distribution, metabolism, and excretion (ADME); the PD-related targets were obtained from the Genecards, OMIM, TTD, and DRUGBANK databases. We used R to take the intersection of LWDH- and PD-related targets and Cytoscape software to construct the drug-component-target network. Moreover, STRING and Cytoscape software was used to analyze protein-protein interactions (PPI), construct a PPI network, and explore potential protein functional modules in the network. The Metascape platform was used to perform KEGG pathway and GO function enrichment analyses. Finally, molecular docking was performed to verify whether the compound and target have good binding activity. RESULTS: After screening and deduplication, 210 effective active ingredients, 204 drug targets, 4333 disease targets, and 162 drug-disease targets were obtained. We consequently constructed a drug-component-targets network and a PPI-drug-disease-targets network. The results showed that the hub components of LWDH pills were quercetin, stigmasterol, kaempferol, and beta-sitosterol; the hub targets were AKT1, VEGFA, and IL6. GO and KEGG enrichment analyses showed that these targets are involved in neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic processes, membrane rafts, MAPK signaling pathways, cellular senescence, and other biological processes. Molecular docking showed that the hub components were in good agreement with the hub targets. CONCLUSION: LWDH pills have implications for the treatment of PD since they contain several active components, target multiple ligands, and activate various pathways. The hub components possibly include quercetin, stigmasterol, kaempferol, and beta-sitosterol and act through pairing with hub targets, such as AKT1, VEGFA, and IL6, to regulate neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic process, membrane raft, MAPK signaling pathway, and cellular senescence for the treatment of PD.

Quantitative structure-activity relationship models for genotoxicity prediction based on combination evaluation strategies for toxicological alternative experiments.

Mutagenicity exerts adverse effects on humans. Conventional methods cannot simultaneously predict the toxicity of a large number of compounds. Most mutagenicity prediction models are based on a single experimental type and lack other experimental combination data as support, resulting in limited application scope and predictive ability. In this study, we partitioned data from GENE-TOX, CPDB, and Chemical Carcinogenesis Research Information System according to the weight-of-evidence method for modelling. In our data set, in vivo and in vitro experiments in groups as well as prokaryotic and eukaryotic cell experiments were included in accordance with the ICH guideline. We compared the two experimental combinations mentioned in the weight-of-evidence method and reintegrated the experimental data into three groups. Nine sub-models and three fusion models were established using random forest (RF), support vector machine (SVM), and back propagation (BP) neural network algorithms. When fusing base models under the same algorithm according to the ensemble rules, all models showed excellent predictive performance. The RF, SVM, and BP fusion models reached a prediction accuracy rate of 83.4%, 80.5%, 79.0% respectively. The area under the curve (AUC) reached 0.853, 0.897, 0.865 respectively. Therefore, the established fusion QSAR models can serve as an early warning system for mutagenicity of compounds.

Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma.

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

A Network Pharmacology-Based Study of the Molecular Mechanisms of Shaoyao-Gancao Decoction in Treating Parkinson's Disease.

Parkinson's disease (PD) is another major neurodegenerative disorder following Alzheimer's disease, which not only seriously reduces the survival in patients, affecting patient's quality of life, but also imposes a tremendous burden on families and even the whole society. It is urgent to find out effective drugs without side effects. The present study applied a creative approach called network pharmacology to explore the active compounds and therapeutic targets of Shaoyao-Gancao Decoction (SYGCD) for treating PD. We identified a total of 48 active compounds mediating 30 PD-related targets to exert synergism, and the same target can be enriched in multiple signal pathways and biological processes, expounding that the decoction can exert synergistic effect on PD by multi-targets and multi-pathways. Furthermore, the molecular docking analysis showed that active compounds and targets can be well combined. These results highlighted the molecular mechanisms underlying the efficiency of SYGCD for PD treatment at a systematic level, investigating thoroughly the innovative therapeutic tactics for PD in traditional Chinese medicine.

Anti-tumor activities and mechanism study of α-pinene derivative in vivo and in vitro.

In previous study, we designed novel α-pinene derivatives based on theories of bioalkylating agents using α-pinene as lead compound and patented these compounds, in which compound α-pinene derivative GY-1 (6,6-dimethylbicyclo[3.1.1]hept-2-en-2-yl)methyl-4-methylbenzenesulfonat) showed strongest inhibition on hepatoma carcinoma cell BEL-7402. In this study, we investigated effect of GY-1 on hepatocellular carcinoma in vitro and in vivo, and explored its mechanism of anti-hepatoma. The results showed that GY-1 showed good anti-liver cancer activity with the IC50 of 84.7 µmol/L in vitro, inhibited tumor growth in vivo with dose-dependent, and GY-1 could arrest the growth of hepatoma cells in the S phase and induced apoptosis in hepatoma cells, down-regulated the expression of C-myc, CDK2 and CyclinE, and up-regulate p53.

A Novel Centralized Range-Free Static Node Localization Algorithm with Memetic Algorithm and Lévy Flight.

Node localization, which is formulated as an unconstrained NP-hard optimization problem, is considered as one of the most significant issues of wireless sensor networks (WSNs). Recently, many swarm intelligent algorithms (SIAs) were applied to solve this problem. This study aimed to determine node location with high precision by SIA and presented a new localization algorithm named LMQPDV-hop. In LMQPDV-hop, an improved DV-Hop was employed as an underground mechanism to gather the estimation distance, in which the average hop distance was modified by a defined weight to reduce the distance errors among nodes. Furthermore, an efficient quantum-behaved particle swarm optimization algorithm (QPSO), named LMQPSO, was developed to find the best coordinates of unknown nodes. In LMQPSO, the memetic algorithm (MA) and Lévy flight were introduced into QPSO to enhance the global searching ability and a new fast local search rule was designed to speed up the convergence. Extensive simulations were conducted on different WSN deployment scenarios to evaluate the performance of the new algorithm and the results show that the new algorithm can effectively improve position precision.

Application of data mining methods to improve screening for the risk of early gastric cancer.

BACKGROUND: Although gastric cancer is a malignancy with high morbidity and mortality in China, the survival rate of patients with early gastric cancer (EGC) is high after surgical resection. To strengthen diagnosing and screening is the key to improve the survival and life quality of patients with EGC. This study applied data mining methods to improve screening for the risk of EGC on the basis of noninvasive factors, and displayed important influence factors for the risk of EGC. METHODS: The dataset was derived from a project of the First Hospital Affiliated Guangdong Pharmaceutical University. A series of questionnaire surveys, serological examinations and endoscopy plus pathology biopsy were conducted in 618 patients with gastric diseases. Their risk of EGC was categorized into low and high risk of EGC by the results of endoscopy plus pathology biopsy. The synthetic minority oversampling technique (SMOTE) was used to solve imbalance categories of the risk of EGC. Four classification models of the risk of EGC was established, including logistic regression (LR) and three data mining algorithms. RESULTS: The three data mining models had higher accuracy than the LR model. Gain curves of the three data mining models were convexes more closer to ideal curves by contrast with that of the LR model. AUC of the three data mining models were larger than that of the LR model as well. The three data mining models predicted the risk of EGC more effectively in comparison with the LR model. Moreover, this study found 16 important influence factors for the risk of EGC, such as occupations, helicobacter pylori infection, drinking hot water and so on. CONCLUSIONS: The three data mining models have optimal predictive behaviors over the LR model, therefore can effectively evaluate the risk of EGC and assist clinicians in improving the diagnosis and screening of EGC. Sixteen important influence factors for the risk of EGC were illustrated, which may helpfully assess gastric carcinogenesis, and remind to early prevention and early detection of gastric cancer. This study may also be conducive to clinical researchers in selecting and conducting the optimal predictive models.

A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters.

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2 = 0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

[A nonlinear multi-compartment lung model for optimization of breathing airflow pattern].

It is difficult to select the appropriate ventilation mode in clinical mechanical ventilation. This paper presents a nonlinear multi-compartment lung model to solve the difficulty. The purpose is to optimize respiratory airflow patterns and get the minimum of the work of inspiratory phrase and lung volume acceleration, minimum of the elastic potential energy and rapidity of airflow rate changes of expiratory phrase. Sigmoidal function is used to smooth the respiratory function of nonlinear equations. The equations are established to solve nonlinear boundary conditions BVP, and finally the problem was solved with gradient descent method. Experimental results showed that lung volume and the rate of airflow after optimization had good sensitivity and convergence speed. The results provide a theoretical basis for the development of multivariable controller monitoring critically ill mechanically ventilated patients.

Pharmacokinetics, tissue distribution, excretion, and antiviral activity of pegylated recombinant human consensus interferon-α variant in monkeys, rats and guinea pigs.

The study aims to characterize the pharmacokinetic, tissue distribution, excretion, and antiviral activity properties of a novel pegylated recombinant human consensus interferon-α variant (PEG-IFN-SA) following a single subcutaneous administration to monkeys, rats and guinea pigs. Studies included: (1) pharmacokinetic properties of PEG-IFN-SA and comparison with those of non-pegylated IFN-SA in rhesus monkeys and rats; (2) tissue distribution and urinary, fecal, and biliary excretion patterns of (125)I-PEG-IFN-SA in guinea pigs; and (3) antiviral activity assessment of PEG-IFN-SA in cynomolgus monkeys. The pegylated protein exhibited improved pharmacokinetic properties compared to IFN-SA in both monkeys and rats, with a 12-fold and 15-fold increase in elimination half-life, and a 100-fold and 10-fold decrease in serum clearance, as well as a 2.5-fold and 10-fold increase in the time to reach peak serum concentration, respectively. (125)I-PEG-IFN-SA was found to be distributed to most of the tissues examined and has character of targeting special distribution, and urinary appeared to be a major route for the excretion of PEG-IFN-SA in guinea pigs. Serum sample analysis from PEG-IFN-SA-treated monkeys showed dose-dependent antiviral activity for one week. These findings demonstrate that pegylation of IFN-SA results in more desirable pharmacokinetic properties, enhanced drug exposure and sustained-efficacy of in vivo antiviral action.

A 30-day preclinical safety evaluation study of recombinant human follicle-stimulating hormone in female rhesus monkeys.

The objective of this study was to identify potential target organs for toxicity of recombinant human follicle stimulating hormone (r-hFSH) in female rhesus monkeys and to establish a no observed adverse effect level (NOAEL). In all, 24 female rhesus monkeys (Chinese origin, weighing 3.4-5.2 kg, approximately 5 years of age) received repeated subcutaneous (sc) r-hFSH at doses of 10, 60, and 300 IU/kg per d or vehicle once daily for 30 days followed by a 15-day recovery period. Endometrial hyperplasia and dermal edema in the external genitals were found in some animals at 300 IU/kg per d. Pharmacologic-related multiple cystic follicles were found in all r-hFSH-treated groups. A weak, anti-FSH antibody response was detected at the end of treatment in animals administered 60 and 300 IU/kg per d. These results indicate that the primary effects of r-hFSH in female rhesus monkeys were related to its pharmacological activity on the reproductive system. The NOAEL was considered to be 60 IU/kg per d.