Core Producer Prices in the United States decreased to 147.83 points in August from 148.01 points in July of 2025. This dataset provides - United States Core Producer Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Core Producer Prices MoM in the United States decreased to -0.10 percent in August from 0.70 percent in July of 2025. This dataset includes a chart with historical data for the United States Core Producer Prices MoM.

Core Producer Prices in the United Kingdom increased to 142.10 points in June from 141.90 points in May of 2025. This dataset provides - United Kingdom Core Producer Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for CORE PRODUCER PRICES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.

Core Producer Prices MoM in the United Kingdom decreased to 0.20 percent in June from 1.10 percent in May of 2025. This dataset includes a chart with historical data for the United Kingdom Core Producer Prices MoM.

Graph and download economic data for Producer Price Index by Commodity: All Commodities (PPIACO) from Jan 1913 to Aug 2025 about commodities, PPI, inflation, price index, indexes, price, and USA.

Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Same Mth PY=100 data was reported at 113.100 Same Mth PY=100 in Oct 2018. This records a decrease from the previous number of 113.900 Same Mth PY=100 for Sep 2018. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Same Mth PY=100 data is updated monthly, averaging 108.600 Same Mth PY=100 from Jan 2010 (Median) to Oct 2018, with 106 observations. The data reached an all-time high of 121.100 Same Mth PY=100 in Apr 2011 and a record low of 87.600 Same Mth PY=100 in Aug 2015. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Same Mth PY=100 data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.I021: Producer Price Index: Core Inflation.

Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Dec2015=100 data was reported at 135.900 Dec2015=100 in Sep 2018. This records an increase from the previous number of 133.500 Dec2015=100 for Aug 2018. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Dec2015=100 data is updated monthly, averaging 122.950 Dec2015=100 from Feb 2017 (Median) to Sep 2018, with 18 observations. The data reached an all-time high of 135.900 Dec2015=100 in Sep 2018 and a record low of 115.300 Dec2015=100 in Jun 2017. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Dec2015=100 data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.I021: Producer Price Index: Core Inflation.

Core Producer Prices YoY in the United Kingdom remained unchanged at 3 percent in June. This dataset includes a chart with historical data for the United Kingdom Core Producer Prices YoY.

Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Prev Dec=100 data was reported at 109.500 Prev Dec=100 in Oct 2018. This records an increase from the previous number of 108.800 Prev Dec=100 for Sep 2018. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Prev Dec=100 data is updated monthly, averaging 103.900 Prev Dec=100 from Jan 2010 (Median) to Oct 2018, with 106 observations. The data reached an all-time high of 115.000 Prev Dec=100 in May 2011 and a record low of 91.900 Prev Dec=100 in Feb 2015. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Prev Dec=100 data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.I021: Producer Price Index: Core Inflation.

Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Dec2010=100 data was reported at 142.500 Dec2010=100 in Dec 2016. This records a decrease from the previous number of 142.600 Dec2010=100 for Nov 2016. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Dec2010=100 data is updated monthly, averaging 122.900 Dec2010=100 from Jan 2012 (Median) to Dec 2016, with 57 observations. The data reached an all-time high of 142.600 Dec2010=100 in Nov 2016 and a record low of 114.400 Dec2010=100 in Jan 2012. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Dec2010=100 data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.I021: Producer Price Index: Core Inflation. Rebased from Dec2010=100 to Dec2015=100 Replacement series ID: 386602147

Core Producer Prices YoY in the United States decreased to 2.80 percent in August from 3.40 percent in July of 2025. This dataset includes a chart with historical data for the United States Core Producer Prices YoY.

Producer Prices in the United States decreased to 149.16 points in August from 149.34 points in July of 2025. This dataset provides the latest reported value for - United States Producer Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Producer Prices in the United States increased 2.60 percent in August of 2025 over the same month in the previous year. This dataset provides - United States Producer Prices Change - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Background:Macleaya cordata extract (MCE) is effective in the treatment of enteritis, but its mechanism has not been fully elucidated. Therefore, this study combined network pharmacology and molecular docking technologies to investigate the potential pharmacological mechanism of MCE in the treatment of enteritis.Methods: The information of active compounds in MCE was accessed through the literature. Furthermore, PubChem, PharmMapper, UniProt, and GeneCards databases were used to analyze the targets of MCE and enteritis. The intersection of drug and disease targets was imported into the STRING database, and the analysis results were imported into Cytoscape 3.7.1 software to construct a protein–protein interaction (PPI) network and to screen core targets. The Metascape database was used for conducting Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. AutoDock Tools software was used for the molecular docking of active compounds with the core targets.Results: MCE has four active compounds, namely, sanguinarine, chelerythrine, protopine, and allocryptopine, and a total of 269 targets after de-duplication. Furthermore, a total of 1,237 targets were associated with enteritis, 70 of which were obtained by aiding the drug–disease intersection with the aforementioned four active compound targets of MCE. Five core targets including mitogen-activated protein kinase 1 (MAPK1) and AKT serine/threonine kinase 1 (AKT1) were obtained using the PPI network, which are considered the potential targets for the four active compounds of MCE in the treatment of enteritis. The GO enrichment analysis involved 749 biological processes, 47 cellular components, and 64 molecular functions. The KEGG pathway enrichment analysis revealed 142 pathways involved in the treatment of enteritis by the four active compounds of MCE, among which PI3K-Akt and MAPK signaling pathways were the most important pathways. The results of molecular docking showed that the four active compounds demonstrated good binding properties at the five core targets.Conclusion: The pharmacological effects of the four active compounds of MCE in the treatment of enteritis involve acting on signaling pathways such as PI3K-Akt and MAPK through key targets such as AKT1 and MAPK1, thus providing new indications for further research to verify its mechanisms.

Background:Macleaya cordata extract (MCE) is effective in the treatment of enteritis, but its mechanism has not been fully elucidated. Therefore, this study combined network pharmacology and molecular docking technologies to investigate the potential pharmacological mechanism of MCE in the treatment of enteritis.Methods: The information of active compounds in MCE was accessed through the literature. Furthermore, PubChem, PharmMapper, UniProt, and GeneCards databases were used to analyze the targets of MCE and enteritis. The intersection of drug and disease targets was imported into the STRING database, and the analysis results were imported into Cytoscape 3.7.1 software to construct a protein–protein interaction (PPI) network and to screen core targets. The Metascape database was used for conducting Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. AutoDock Tools software was used for the molecular docking of active compounds with the core targets.Results: MCE has four active compounds, namely, sanguinarine, chelerythrine, protopine, and allocryptopine, and a total of 269 targets after de-duplication. Furthermore, a total of 1,237 targets were associated with enteritis, 70 of which were obtained by aiding the drug–disease intersection with the aforementioned four active compound targets of MCE. Five core targets including mitogen-activated protein kinase 1 (MAPK1) and AKT serine/threonine kinase 1 (AKT1) were obtained using the PPI network, which are considered the potential targets for the four active compounds of MCE in the treatment of enteritis. The GO enrichment analysis involved 749 biological processes, 47 cellular components, and 64 molecular functions. The KEGG pathway enrichment analysis revealed 142 pathways involved in the treatment of enteritis by the four active compounds of MCE, among which PI3K-Akt and MAPK signaling pathways were the most important pathways. The results of molecular docking showed that the four active compounds demonstrated good binding properties at the five core targets.Conclusion: The pharmacological effects of the four active compounds of MCE in the treatment of enteritis involve acting on signaling pathways such as PI3K-Akt and MAPK through key targets such as AKT1 and MAPK1, thus providing new indications for further research to verify its mechanisms.

Large-scale data sets of protein-protein interactions (PPIs) are a valuable resource for mapping and analysis of the topological and dynamic features of interactome networks. The currently available large-scale PPI data sets only contain information on interaction partners. The data presented in this study also include the sequences involved in the interactions (i.e., the interacting regions, IRs) suggested to correspond to functional and structural domains. Here we present the first large-scale IR data set obtained using mRNA display for 50 human transcription factors (TFs), including 12 transcription-related proteins. The core data set (966 IRs; 943 PPIs) displays a verification rate of 70%. Analysis of the IR data set revealed the existence of IRs that interact with multiple partners. Furthermore, these IRs were preferentially associated with intrinsic disorder. This finding supports the hypothesis that intrinsically disordered regions play a major role in the dynamics and diversity of TF networks through their ability to structurally adapt to and bind with multiple partners. Accordingly, this domain-based interaction resource represents an important step in refining protein interactions and networks at the domain level and in associating network analysis with biological structure and function.

ObjectiveThe aim of this study is to use network pharmacology and data mining to explore the role of traditional Chinese medicine (TCM) in ischemic stroke (IS) intervention by ferroptosis regulation. The results will provide reference for related research on ferroptosis in IS.MethodsThe ferroptosis-related targets were obtained from the GeneCards, GeneCLiP3, and FerrDdb databases, while the IS targets were sourced from the GeneCards and DisGeNET databases. Venny was used to identify IS targets associated with ferroptosis. A protein-protein interaction (PPI) analysis was then conducted, and machine learning screening was used to validate these potential targets. The potential targets that met specific criteria and their related compounds allowed us to select TCMs. A mechanistic analysis of the potential targets was conducted using the DAVID database. PPI network diagrams, target-compound network diagrams, and target-compound-TCM network diagrams were then constructed. Finally, molecular docking technology was used to verify the binding activities of the TCM compounds and core components with the identified targets. In addition, the properties, flavors, meridian tropism, and therapeutic effects of the candidate TCMs were analyzed and statistically evaluated.ResultsA total of 706 targets associated with ferroptosis in IS were obtained, and 14 potential ferroptosis targets in IS were obtained using machine learning. Furthermore, 413 compounds and 301 TCMs were screened, and the binding activities of the targets to the TCM compounds and the core prescriptions were stable. The candidate TCMs primarily exhibited cold, warm, bitter taste, pungent taste, liver meridian, heat-cleaning medicinal, and tonify deficiency properties.ConclusionsThis study investigated ferroptosis regulation for IS intervention using TCM. We began by investigating the targets of IS and ferroptosis, and we also analyzed the relevant mechanism of ferroptosis in IS. The results of this study provide reference for related research on ferroptosis in IS.

BackgroundFriedreich's ataxia (FRDA) is a familial hereditary disorder that lacks available therapy. Therefore, the identification of novel biomarkers and key mechanisms related to FRDA progression is urgently required.MethodsWe identified the up-regulated and down-regulated differentially expressed genes (DEGs) in children and adult FRDA from the GSE11204 dataset and intersected them to determine the co-expressed DEGs (co-DEGs). Enrichment analysis was conducted and a protein-protein interaction (PPI) network was constructed to identify key pathways and hub genes. The potential diagnostic biomarkers were validated using the GSE30933 dataset. Cytoscape was applied to construct interaction and competitive endogenous RNA (ceRNA) networks.ResultsGene Set Enrichment Analysis (GSEA) indicated that the genes in both the child and adult samples were primarily enriched in their immune-related functions. We identified 88 co-DEGs between child and adult FRDA samples. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome enrichment analysis suggested that these co-DEGs were primarily enriched in immune response, inflammatory reaction, and necroptosis. Immune infiltration analysis showed remarkable differences in the proportions of immune cell subtype between FRDA and healthy samples. In addition, ten core genes and one gene cluster module were screened out based on the PPI network. We verified eight immune-specific core genes using a validation dataset and found CD28, FAS, and ITIF5 have high diagnostic significance in FRDA. Finally, NEAT1-hsa-miR-24-3p-CD28 was identified as a key regulatory pathway of child and adult FRDA.ConclusionsDownregulation of three immune-specific hub genes, CD28, FAS, and IFIT5, may be associated with the progression of child and adult FRDA. Furthermore, NEAT1-hsa-miR-24-3p-CD28 may be the potential RNA regulatory pathway related to the pathogenesis of child and adult FRDA.