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.

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 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.

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.

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.

Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Prev Month=100 data was reported at 100.700 Prev Mth=100 in Oct 2018. This records a decrease from the previous number of 101.100 Prev Mth=100 for Sep 2018. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Prev Month=100 data is updated monthly, averaging 100.600 Prev Mth=100 from Jan 2010 (Median) to Oct 2018, with 106 observations. The data reached an all-time high of 105.200 Prev Mth=100 in Jan 2011 and a record low of 94.100 Prev Mth=100 in Feb 2015. Kazakhstan PPI: Core Inflation: excl Oil & Metals Prices: Prev Month=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 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.

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.

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

Ligand binding to the cell surface receptors initiates signaling cascades that are commonly transduced through a protein–protein interaction (PPI) network to activate a plethora of response pathways. However, tools to capture the membrane PPI network are lacking. Here, we describe a cross-linking-aided mass spectrometry workflow for isolation and identification of signal-dependent epidermal growth factor receptor (EGFR) proteome. We performed protein cross-linking in cell culture at various time points following EGF treatment, followed by immunoprecipitation of endogenous EGFR and analysis of the associated proteins by quantitative mass spectrometry. We identified 140 proteins with high confidence during a 2 h time course by data-dependent acquisition and further validated the results by parallel reaction monitoring. A large proportion of proteins in the EGFR proteome function in endocytosis and intracellular protein transport. The EGFR proteome was highly dynamic with distinct temporal behavior; 10 proteins that appeared in all time points constitute the core proteome. Functional characterization showed that loss of the FYVE domain-containing proteins altered the EGFR intracellular distribution but had a minor effect on EGFR proteome or signaling. Thus, our results suggest that the EGFR proteome include functional regulators that influence EGFR signaling and bystanders that are captured as the components of endocytic vesicles. The high-resolution spatiotemporal information of these molecules facilitates the delineation of many pathways that could determine the strength and duration of the signaling, as well as the location and destination of the receptor.

Switzerland PPI: Core Prices data was reported at 100.015 Dec2015=100 in Oct 2018. This records a decrease from the previous number of 100.102 Dec2015=100 for Sep 2018. Switzerland PPI: Core Prices data is updated monthly, averaging 103.357 Dec2015=100 from May 2003 (Median) to Oct 2018, with 186 observations. The data reached an all-time high of 108.282 Dec2015=100 in Nov 2008 and a record low of 98.222 Dec2015=100 in Aug 2017. Switzerland PPI: Core Prices data remains active status in CEIC and is reported by Swiss Federal Statistical Office. The data is categorized under Global Database’s Switzerland – Table CH.I011: Producer Price Index: December 2015=100.

Producer Price Inflation 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 Producer Price Inflation MoM.

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.

BackgroundContrast-induced nephropathy (CIN) is increasingly seen in patients receiving contrast medium. Abelmoschus manihot (L.) Medik. (Malvaceae) and its preparations are widely used and effective in the treatment of various chronic kidney diseases and CIN in China. It is supposed to be an important adjuvant therapy for CIN.MethodsPubMed and CNKI were searched for the main compounds of A. manihot L. The Swiss target prediction platform, OMIM, GeneCards, DisGeNET, and DrugBank databases were mined for information relevant to the prediction of targets that A. manihot L. in the treatment of CIN. Subsequently, STRING database was applied for the construction of the PPI protein interaction network, meanwhile, the core targets were screened. DAVID database was used to perform the GO function and Kegg signal pathway enrichment analysis. AutoDockTools and PYMOAL were used for molecular docking. Vitro experiments were used to verify the effect of TFA, the main active component of A. manihot L., in the intervention of iopromide-induced cells injury.ResultsA total of 17 chemical components and 133 potential targets in A. manihot L. were obtained. The top 15 proteins with higher degree value were selected from the PPI network model, AKT1, PIK3R1, EGFR, SRC,AR, APP, TNF, GAPDH, MMP9, and PTPN1, etc. may be core targets. The enrichment analysis indicated that A. manihot L. was involved in the regulation of PI3K/AKT signaling pathway, FoxO signaling pathway, VEGF signaling pathway, HIF-1, TNF signaling pathway, melanoma, hepatitis B, and other signaling pathways which were mainly associated with the regulation of transcription and apoptosis, protein phosphorylation, inflammatory response, aging, and cell proliferation. Molecular docking indicated that the key components and core targets had a good binding ability. The vitro experiments illustrated that TFA reduces iopromide induced renal tubular cell injury and apoptosis, which may be related to regulating the phosphorylation of AKT.ConclusionThe study preliminarily revealed the multi-component, multi-target, and multi-pathway synergistic effects of A. manihot L. on CIN, which provide theoretical reference and basis for the study of the pharmacological mechanism of A. manihot L. in the treatment of CIN.

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.

(A)We use microarray experiments to study differential gene expressions under specific disease conditions. (B.1)The DEGs are mapped to PPI networks using STRING database to get time-stamped PPI networks for every mouse-prion models. (B.2)Protein networks are used to identify potential disease related genes. (B.3)The identified shared DEGs are then used to identify genes potentially participating in crosstalk. These potential crosstalk genes are then mapped to KEGG database to identify a consensus bow-tie network. (C)Mathematical modeling of the identified bow-tie network using ordinary differential equations. (D) Prediction of the activities of the network components during disease condition. (E)Validation of predicted differential gene expression by comparing it with the microarray results. *The microarray experiments were performed in Hwang et al. [5].

Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.

Consumer Price Index CPI in the United States increased to 324.80 points in September from 323.98 points in August of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.