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At least 91 records · Page 5Linked to original sources
PubMed2025

A comprehensive integration of data on the association of ITPKC polymorphisms with susceptibility to Kawasaki disease: a meta-analysis.

BACKGROUND: This study aims to conduct a comprehensive meta-analysis of existing research to define clear associations between variations in the ITPKC gene and the risk of developing Kawasaki disease (KD). METHODS: A comprehensive search was conducted across multiple databases, including but not limited to PubMed, Scopus, EMBASE, and CNKI, up to June 1, 2024, to gather relevant information. This search utilized keywords and MeSH terms related to hyperbilirubinemia and genetic factors. The inclusion criteria encompassed original case-control, longitudinal, or cohort studies. Correlations were analyzed as odds ratios (ORs) with 95% confidence intervals (CIs) using Comprehensive Meta-Analysis software. RESULTS: Eighteen case-control studies with 5,434 KD cases and 9,419 controls were analyzed. Of these, ten studies assessed 3,129 KD cases and 6,172 controls for the rs28493229 variant, four examined 1,039 cases and 1,688 controls for the rs2290692 variant, two focused on 595 cases and 820 controls for the rs7251246 variant, and two investigated 671 cases and 739 controls for the rs10420685 variant. Results showed a significant association between the rs28493229 polymorphism and increased KD risk across all five genetic models. Subgroup analysis indicated this polymorphism correlates with KD susceptibility in Asians but not in the Chinese population. In contrast, no associations were found between the rs2290692, rs7251246, and rs10420685 polymorphisms and KD risk. CONCLUSIONS: Our pooled data indicate a significant association between the ITPKC rs28493229 polymorphism's minor allele and an increased risk of developing KD, suggesting this variant may enhance susceptibility. Conversely, SNPs rs2290692, rs7251246, and rs10420685 do not demonstrate a statistically significant relationship with KD.

Humans↗
PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗
PubMed2025

Multi-omics technologies: Novel tools and methods for assessing nerve injury and regeneration.

Recently, with the rapid advancement of multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, new tools and approaches have been introduced for studying nerve injury and regeneration. This review highlights the application and progress of multi-omics in uncovering the mechanisms of nerve injury, guiding the development of regenerative strategies, and promoting clinical translation. By integrating multi-omics datasets, researchers can comprehensively track dynamic molecular changes following nerve injury, including abnormal gene expression, disrupted protein signaling, altered metabolic programs, and shifts in the immune microenvironment. Single-cell multi-omics technologies resolve cellular heterogeneity, revealing the distinct functions of neurons, glial cells, and immune cell subpopulations during the injury response. Spatially resolved transcriptomics maintain the spatial context of lesion and regeneration sites, enabling precise localization for targeted interventions. Multi-omics technologies not only identify key molecular players involved in nerve regeneration but also create opportunities for personalized medicine. Nonetheless, integrating multi-omics data poses technical challenges, including high dimensionality, batch effects, and algorithmic constraints, while ethical concerns related to stem cell therapy and gene editing require stringent oversight. To transition from structural reconstruction to functional remodeling, future research should emphasize artificial intelligence-driven data integration, organ-on-a-chip modeling, and cross-disciplinary collaboration to overcome existing technical barriers and accelerate the clinical application of neuroregenerative therapies.

artificial intelligence↗
PubMed1990

[Noninvasive hemodynamic monitoring via the integration of data obtained by ECG, aortic flow by Doppler esophageal probe and by finger plethysmography].

The aims of this work are to describe the general and technical characteristics of a new device for the noninvasive monitoring of patients in intensive care and during general anaesthesia, and the results concerning the reliability of this method. An ultrasonic esophageal probe and an echo-Doppler device have been used to obtain continuous data of the aortic diameter and of blood velocity. Aortic output is calculated automatically. This method, together with other non-invasive monitoring techniques (blood pressure, heart rate, rhythm and cardiac conduction), gives on the one hand the data of aortic output, systemic peripheral resistance and stroke volume; on the other, through a computerized elaboration, the systolic time intervals (PEP pre-ejection period, LVET left ventricular ejection time, QS2 electromechanical systole, PEP/LVET ratio of PEP to LVET). The validation of STI data, has been obtained through 125 comparative measurements for each of the three parameters. The data obtained through the aortic velocity waveform in descending aorta (pulsed Doppler) have been compared with those obtained through the aortic pressure waveform (intra aortic catheter). The correlation was: PEP 0.92, LVET 0.95, QS2 0.93. The clinical application of this method supplies data concerning cardiac load, after-load and indirectly cardiac pre-load. This non-invasive procedure gives us a continuous measurement of hemo-dynamic situation, which allows the physician to plan and evaluate the therapeutical efficacy. Finally new pathologic events may be opportunely faced.

Anesthesia, General↗
PubMed1994

Customized dual data entry for computerized data analysis.

A major responsibility of any Quality Assurance Unit (QUA) is to ensure data integrity. Errors made during data entry can lead to many problems in the study review process and decrease the quality, accuracy, and overall efficiency of data management. One technique that can reduce the number of data entry errors in computer data sets is the use of a dual entry data system. Currently available software allows creation of customized data entry screens that either closely resemble or duplicate the data collection forms used during studies. Two data entry operators enter data into two independent data sets. The use of an on-screen display that resembles the data collection form reduces the potential for keypunch errors. The two data sets can then be electronically compared. The comparison reports differences between the two data sets. When differences exist, the correct values can be determined by reference to the original data sheets and the two data files can then be corrected. Theoretically, the only key punch errors that will exist after making these corrections are when the two independent entry operators make the same exact data entry error. Typically, the time required for two people to enter data is minimal compared to the time required to manually identify and correct data entry discrepancies. With error-free data entry, we have found that electronic data quality, accuracy, and audit efficiency are improved at every subsequent step of data management, analysis, quality assurance auditing, and report generation.

Information Systems↗
PubMed1993

[General principles for safety evaluation of pharmaceuticals in man based on integration of data from various sources].

Safety evaluation of pharmaceuticals consists of two processes; firstly, to grade the adverse effects of a test material on individuals based on scientific evidence, and, secondly, to judge whether the adverse effects occurring under the dose condition of the material capable of exhibiting its efficacy in patients remains within the acceptable safe range. Accordingly, the basic criteria for safety evaluation of pharmaceuticals can be, simply, said to know how high the dose-response curve of adverse effects lies above that of efficacy. On the other hand, the judgement concerning how much difference is necessary between both dose-response curves with regard to the safety would require a careful consideration on a case by case basis taking into account various information on the risk/benefit balance of the drug such as 1) medical usefulness and social needs of the drug and 2) the presumed severity of adverse effects in man.

Data Collection↗