Biomedical subjects

Haiying Wang

Publications and source records attributed to Haiying Wang.

14 recordsLinked to original sources

PubMed2026

HSK21542 for Postoperative Analgesia in Gynecological Surgery: A Pooled Post-Hoc Analysis of Two Phase III Randomized Controlled Trials.

BACKGROUND: Effective postoperative pain management in gynecological surgery is challenging because of complex visceral-somatic pain interactions and the adverse effects of conventional analgesics. HSK21542, a novel peripherally restricted kappa-opioid receptor (KOR) agonist that selectively targets visceral pain pathways enriched with KORs, may provide adequate analgesia without systemic adverse events. METHODS: We conducted a pooled post-hoc analysis of data from two phase III, multicenter, triple-blinded, randomized controlled trials (Study 301, HSK21542 vs placebo; Study 303, HSK21542 vs tramadol vs placebo). Eligible patients undergoing elective gynecological surgery were included. The primary outcome was the summed pain-intensity difference over 12 and 24 hours (SPID 12h and SPID 24h ). Secondary outcomes were pain-relief quality (proportion of patients relieved from severe pain with a pain numerical rating score ≤ 3 between 0 and 24 hours) and rescue-analgesic requirements (number of doses and time to first rescue analgesic). Adverse events were also assessed. RESULTS: A total of 370 patients were analyzed: 150 received HSK21542, 139 received a placebo, and 81 received tramadol. After inverse probability of treatment weighting (IPTW) adjustment, baseline characteristics were well-balanced across treatment groups (all standardized mean differences [SMD] <0.1; see Table 1 for 95% CIs). HSK21542 produced greater reductions in pain intensity over 12 and 24 hours than placebo (least-squares mean differences -8.1 and -16.3 for SPID 12h and SPID 24h , respectively. Both P < .001) and no statistically significant difference was observed between HSK21542 and tramadol ( P > .05). Significantly more patients in the HSK21542 group were relieved from severe pain at 0 to 12 hours (92.7% vs 82.7%, P < .001) and required fewer rescue doses at 0 to 12 hours (0.00 [IQR 0.00-1.00] vs 1.00 [IQR 0.00-2.00], P < .001) and 0 to 24 hours (0.00 [IQR 0.00-1.00] vs 1.00 [IQR 0.00-2.00], P < .001) than those in the placebo group, whereas no significant differences with tramadol both in 0 to 12 and 0 to 24 hours. HSK21542 was also associated with significantly lower incidences of nausea (24.7% vs 66.7%) and vomiting (21.3% vs 60.5%) than tramadol. Only one case of dizziness occurred in the tramadol group. CONCLUSIONS: HSK21542 could provide adequate postoperative analgesia with few adverse events in patients undergoing gynecological surgery.

Humans↗

PubMed2026

Dexamethasone as an adjuvant to continuous erector spinae plane block for postoperative analgesia after video-assisted thoracoscopic surgery for pulmonary nodule surgery: a randomized controlled trial.

BACKGROUND: While dexamethasone is proven to enhance single-shot erector spinae plane block (ESPB), its role as an adjuvant in continuous ESPB catheters is unclear. This randomised controlled trial evaluated whether adding dexamethasone to ropivacaine improves analgesia after video-assisted thoracoscopic surgery (VATS). METHODS: 85 patients undergoing VATS with continuous ESPB were randomised to receive postoperative infusion of either 0.2% ropivacaine(C-ESPB group) or ropivacaine with 10 mg dexamethasone(D + C-ESPB group). The primary outcome was resting pain visual analog scale (VAS)at 12 h postoperatively, while secondary outcomes included QoR-15 scores, tramadol consumption, time to first analgesic requirement, postoperative adverse events, 3-month incidence of chronic pain, catheter-related complications, pain intensity at other times, and hospital stay. RESULTS: The D + C-ESPB group had significantly lower resting pain at 12 h [2.56 (1.03) vs 3.24 (1.21), mean difference -0.680, p = 0.006]; and lower coughing pain at 12 h [4.60 (1.48) vs 5.69 (1.35), mean difference 1.086, p < 0.001], with analgesic superiority sustained through 72 h. Quality of Recovery-15 scores were higher at 12 h [124.70 (12.48) vs 117.26 (12.24); mean difference -7.436, p = 0.007] and 48 h [141.60 (5.51) vs 138.98 (6.64); mean difference -2.628, p = 0.050]; Total tramadol consumption over 72 h was markedly reduce [0 (0,100) vs 100 (75,100), z = -3.807, p < 0.001], and hospital stay was shorter [Mean (SD) 6.09 (1.34) d vs 6.93 (1.55)d, p < 0.001]. The intervention did not, however, alter the 3-month incidence of chronic postsurgical pain (31% vs 34%, p = 0.756). CONCLUSION: Dexamethasone significantly enhances the analgesic efficacy of continuous ESPB, improving early pain control, recovery quality, and opioid-sparing after VATS, but does not reduce the incidence of chronic persistent surgical pain.

Humans↗

PubMed2006

Application of eigenmode in the adaptive optics system based on a micromachined membrane deformable mirror.

The construction process and characteristics of a deformable mirror eigenmode are introduced. The eigenmode of a 37-element micromachined membrane deformable mirror (MMDM) from OKO, Ltd. is analyzed. The Gaussian-Seidel low-order aberrations are fitted with eigenmodes as basic functions. An experimental adaptive optics (AO) system is constructed with the MMDM as the wavefront corrector, a deformable mirror eigenmode as the wavefront control algorithm, and a Shack-Hartmann wavefront sensor as the wavefront detector. The experimental results demonstrate that the deformable mirror eigenmode can act as the wavefront control algorithm for the AO system based on the MMDM.

Journal Article↗

PubMed2006

Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data.

BACKGROUND: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. RESULTS: Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE), this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. In order to understand temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis was firstly performed. By clustering SAGE libraries, a hierarchical tree reflecting relationships between developmental stages was obtained. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. The results indicated that a relatively simple instance-based model (KStar model) performed significantly better than relatively more complex algorithms, e.g. neural networks. To deal with the problem of functional class imbalance occurring in the dataset, two data re-sampling techniques were studied. A random over-sampling method supported the implementation of the most powerful prediction models. The KStar model was also able to achieve higher predictive sensitivities and specificities using random over-sampling techniques. CONCLUSION: The approaches assessed in this paper represent an efficient and relatively inexpensive in silico methodology for supporting large-scale analysis of photoreceptor gene expression by SAGE. They may be applied as complementary methodologies to support functional predictions before implementing more comprehensive, experimental prediction and validation methods. They may also be combined with other large-scale, data-driven methods to facilitate the inference of transcriptional regulatory networks in the developing retina. Furthermore, the methodology assessed may be applied to other data domains.

Animals↗

PubMed2006

rpoB Gene mutations and molecular characterization of rifampin-resistant Mycobacterium tuberculosis isolates from Shandong Province, China.

Sixty rifampin (RIF)-resistant and 75 RIF-susceptible Mycobacterium tuberculosis isolates from Shandong Province, China, were analyzed for rpoB gene mutations and genotyped. Mycobacterial interspersed repetitive unit (MIRU) genotype 223325173533 was overrepresented among RIF-resistant isolates. MIRU combined with IS6110 restriction fragment length polymorphism analysis as the second-line genotyping method may reflect epidemiologic links more reliably than each method alone.

Antibiotics, Antitubercular↗

PubMed2006

Acetylation of p53 at lysine 373/382 by the histone deacetylase inhibitor depsipeptide induces expression of p21(Waf1/Cip1).

Generally, histone deacetylase (HDAC) inhibitor-induced p21(Waf1/Cip1) expression is thought to be p53 independent. Here we found that an inhibitor of HDAC, depsipeptide (FR901228), but not trichostatin A (TSA), induces p21(Waf1/Cip1) expression through both p53 and Sp1/Sp3 pathways in A549 cells (which retain wild-type p53). This is demonstrated by measuring relative luciferase activities of p21 promoter constructs with p53 or Sp1 binding site mutagenesis and was further confirmed by transfection of wild-type p53 into H1299 cells (p53 null). That p53 was acetylated after depsipeptide treatment was tested by sequential immunoprecipitation/Western immunoblot analysis with anti-acetylated lysines and anti-p53 antibodies. The acetylated p53 has a longer half-life due to a significant decrease in p53 ubiquitination. Further study using site-specific antiacetyllysine antibodies and transfection of mutated p53 vectors (K319/K320/K321R mutated and K373R/K382R mutations) into H1299 cells revealed that depsipeptide specifically induces p53 acetylation at K373/K382, but not at K320. As assayed by coimmunoprecipitation, the K373/K382 acetylation is accompanied by a recruitment of p300, but neither CREB-binding protein (CBP) nor p300/CBP-associated factor (PCAF), to the p53 C terminus. Furthermore, activity associated with the binding of the acetylated p53 at K373/K382 to the p21 promoter as well as p21(Waf1/Cip1) expression is significantly increased after depsipeptide treatment, as tested by chromatin immunoprecipitations and Western blotting, respectively. In addition, p53 acetylation at K373/K382 is confirmed to be required for recruitment of p300 to the p21 promoter, and the depsipeptide-induced p53 acetylation at K373/K382 is unlikely to be dependent on p53 phosphorylation at Ser15, Ser20, and Ser392 sites. Our data suggest that p53 acetylation at K373/K382 plays an important role in depsipeptide-induced p21(Waf1/Cip1) expression.

Acetylation↗

PubMed2005

Non-linear mapping for exploratory data analysis in functional genomics.

BACKGROUND: Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory, visualisation-driven approaches. Such approaches should satisfy the following factors: Support for intuitive cluster visualisation, user-friendly and robust application, computational efficiency and generation of biologically meaningful outcomes. This research assesses a relaxation method for non-linear mapping that addresses these concerns. Its applications to gene expression and protein-protein interaction data analyses are investigated. RESULTS: Publicly available expression data originating from leukaemia, round blue-cell tumours and Parkinson disease studies were analysed. The method distinguished relevant clusters and critical analysis areas. The system does not require assumptions about the inherent class structure of the data, its mapping process is controlled by only one parameter and the resulting transformations offer intuitive, meaningful visual displays. Comparisons with traditional mapping models are presented. As a way of promoting potential, alternative applications of the methodology presented, an example of exploratory data analysis of interactome networks is illustrated. Data from the C. elegans interactome were analysed. Results suggest that this method might represent an effective solution for detecting key network hubs and for clustering biologically meaningful groups of proteins. CONCLUSION: A relaxation method for non-linear mapping provided the basis for visualisation-driven analyses using different types of data. This study indicates that such a system may represent a user-friendly and robust approach to exploratory data analysis. It may allow users to gain better insights into the underlying data structure, detect potential outliers and assess assumptions about the cluster composition of the data.

Algorithms↗

PubMed2005

Multiple SVM-RFE for gene selection in cancer classification with expression data.

This paper proposes a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE). Unlike the SVM-RFE method, at each step, the proposed approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. We tested the proposed method on four gene expression datasets for cancer classification. The results show that the proposed feature selection method selects better gene subsets than the original SVM-RFE and improves the classification accuracy. A Gene Ontology-based similarity assessment indicates that the selected subsets are functionally diverse, further validating our gene selection method. This investigation also suggests that, for gene expression-based cancer classification, average test error from multiple partitions of training and test sets can be recommended as a reference of performance quality.

Algorithms↗

PubMed2004

An integrative and interactive framework for improving biomedical pattern discovery and visualization.

Recent progress in medical sciences has led to an explosive growth of data. Due to its inherent complexity and diversity, mining such volumes of data to extract relevant knowledge represents an enormous challenge and opportunity. Interactive pattern discovery and visualization systems for biomedical data mining have received relatively little attention. Emphasis has been traditionally placed on automation and supervised classification problems. Based on self-adaptive neural networks and pattern-validation statistical tools, this paper presents a user-friendly platform to support biomedical pattern discovery and visualization. It has been tested on several types of biomedical data, such as dermatology and cardiology data sets. The results indicate that in comparison to traditional techniques, such as Kohonen Maps, this platform may significantly improve the effectiveness and efficiency of pattern discovery and classification tasks, including problems described by several classes. Furthermore, this study shows how the combination of graphical and statistical tools may make these patterns more meaningful.

Algorithms↗

PubMed2004

[Effect of conjugated linoleic acid on PPAR gamma gene expression and serum leptin in obese rat].

OBJECTIVE: In order to study the effect of conjugated linoleic acid on PPAR gamma gene expression, serum leptin, blood glucose and blood lipid level. METHODS: Five groups of male Wistar rats were randomly selected and fed with different diets (basic diet, high-fat diet, high-fat with 0.75% CLA, high-fat with 1.50% CLA, and high-fat with 3.00% CLA) for 12 weeks. At the end of experiment, the rats were sacrificed, the ratio of lipids to body weight were calculated, and the blood glucose, serum lipids, serum leptin were measured. RT-PCR was used for measuring the expression of PPAR gamma mRNA in white adipose tissue of rats. RESULTS: It was shown that the supplement of CLA could decrease decreased blood glucose, serum triglyceride, cholesterol and leptin levels, and increased the expression of PPAR gamma in white adipose tissue of obese rat. CONCLUSION: CLA could decrease blood glucose, serum lipids, and decrease the levels of leptin possibly via activating peroxisome proliferator--activated receptor gamma (PPAR gamma), resulting in the improvement of leptin resistance of obese rat.

Adipose Tissue↗

PubMed2004

[Effect of dietary mineral nutrients and vitamins on metabolism of rat fed with high fat].

OBJECTIVE: To explore dietary mineral nutrients (calcium, magnesium, zinc, ferric, chromium) and vitamins (vitamin A, B1, B2, C, D, E) on the effect of metabolic disorder of Wistar rat fed with high fat diet. METHODS: Fifty obesity-prone male rats were randomly divided into 5 groups: A group were fed with basic diet, B group were fed with high fat diet and normal minerals and vitamins, C group were fed with high fat diet with lower minerals and vitamins, D group were fed with high fat diet with higher minerals and vitamins, E group were fed with high fat diet with only higher calcium. After 8 weeks, blood glucose, serum insulin and leptin, serum triglycerides, cholesterol and high-density lipoprotein were measured respectively. RESULTS: The body weight and fat content in B group were significantly higher than those of other groups. The body weight in D, E group decreased significantly compared with B group, both of which are similar with A group. In the case of blood glucose, serum insulin, leptin,triglycerides, cholesterol and high-density lipoprotein, they all have the tendency of decreasing in D group compared with E group. CONCLUSION: In addition to reducing body weight and fat content, dietary mineral nutrients and vitamins can also ameliorate the blood glucose and improve hyperinsulinemia and hyperleptinemia, modify the metabolic disordor of rat fed with high fat diet.

Animals↗

PubMed2003

A markup language for electrocardiogram data acquisition and analysis (ecgML).

BACKGROUND: The storage and distribution of electrocardiogram data is based on different formats. There is a need to promote the development of standards for their exchange and analysis. Such models should be platform-/ system- and application-independent, flexible and open to every member of the scientific community. METHODS: A minimum set of information for the representation and storage of electrocardiogram signals has been synthesised from existing recommendations. This specification is encoded into an XML-vocabulary. The model may aid in a flexible exchange and analysis of electrocardiogram information. RESULTS: Based on advantages of XML technologies, ecgML has the ability to present a system-, application- and format-independent solution for representation and exchange of electrocardiogram data. The distinction between the proposal developed by the U.S Food and Drug Administration and ecgML model is given. A series of tools, which aim to facilitate ecgML-based applications, are presented. CONCLUSIONS: The models proposed here can facilitate the generation of a data format, which opens ways for better and clearer interpretation by both humans and machines. Its structured and transparent organisation will allow researchers to expand and test its capabilities in different application domains. The specification and programs for this protocol are publicly available.

Decision Support Techniques↗

PubMed2002

Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks.

There is an increasing need to develop powerful techniques to improve biomedical pattern discovery and visualization. This paper presents an automated approach, based on hybrid self-adaptive neural networks, to pattern identification and visualization for biomolecular data. The methods are tested on two datasets: leukemia expression data and DNA splice-junction sequences. Several supervised and unsupervised models are implemented and compared. A comprehensive evaluation study of some of their intrinsic mechanisms is presented. The results suggest that these tools may be useful to support biological knowledge discovery based on advanced classification and visualization tasks.

Algorithms↗

PubMed2002

Feature decomposition architectures for neural networks: algorithms, error bounds, and applications.

In recent years, systems consisting of multiple modular neural networks have attracted substantial interest in the neural networks community because of various advantages they offer over a single large monolithic network. In this paper, we propose two basic feature decomposition models (namely, parallel model and tandem model) in which each of the neural network modules processes a disjoint subset of the input features. A novel feature decomposition algorithm is introduced to partition the input space into disjoint subsets solely based on the available training data. Under certain assumptions, the approximation error due to decomposition can be proved to be bounded by any desired small value over a compact set. Finally, the performance of feature decomposition networks is compared with that of a monolithic network in real world bench mark pattern recognition and modeling problems.

Algorithms↗