Exploring the mechanisms and targets of proton pump inhibitors-induced osteoporosis through network toxicology, molecular docking, and molecular dynamics simulations

BackgroundProton pump inhibitors (PPIs) are widely used for the treatment of acid-related disorders, but long-term use has been increasingly associated with an elevated risk of osteoporosis. However, the underlying molecular mechanisms and specific targets of PPIs-induced bone loss remain poorly understood. This study aimed to explore the molecular mechanisms and key genes of PPIs-induced osteoporosis using network toxicology, molecular docking, and molecular dynamics simulations.MethodsWe identified common targets of four widely used PPIs (omeprazole, lansoprazole, pantoprazole, and rabeprazole) and osteoporosis by screening large-scale biological databases. A protein-protein interaction network was constructed, and key hub genes were determined based on topological parameters such as degree, betweenness centrality, and closeness centrality. Enrichment analysis was performed to explore the biological processes, cellular components, molecular functions, and KEGG pathways associated with the overlapping targets. Molecular docking was conducted to evaluate the binding affinities between PPIs and their potential targets, and molecular dynamics simulations were employed to assess the stability of these interactions over time.ResultsWe identified 35 potential targets for omeprazole-induced osteoporosis, 39 for lansoprazole, 29 for pantoprazole, and 29 for rabeprazole. Topological analysis of the protein-protein interaction networks revealed the hub genes for each PPI: epidermal growth factor receptor (EGFR) for omeprazole, estrogen receptor 1 (ESR1) for lansoprazole, EGFR for pantoprazole, and Proto-oncogene tyrosine-protein kinase SRC for rabeprazole. Molecular docking demonstrated strong and stable binding affinities between PPIs and their respective targets, with binding energies all below −5 kcal/mol. Molecular dynamics simulations confirmed the structural stability of these complexes, characterized by low root mean square deviation and root mean square fluctuation values and consistent hydrogen bond formation.ConclusionThis study identified EGFR, ESR1, and SRC as key regulatory genes in PPIs-induced osteoporosis, highlighting their roles in bone metabolism. The stable interactions between PPIs and these targets suggest their involvement in bone loss, providing a foundation for future experimental validation

Development potential of nanoenabled agriculture projected using machine learning

The controllability and targeting of nanoparticles (NPs) offer solutions for precise and sustainable agriculture. However, the development potential of nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 datasets and predict (R2 higher than 0.8 for 13 random forest models) the response and uptake/transport of various NPs by plants using a machine learning approach. Multiway feature importance analysis quantitatively shows that plant responses are driven by the total NP exposure dose and duration and plant age at exposure, as well as the NP size and zeta potential. Feature interaction and covariance analysis further improve the interpretability of the model and reveal hidden interaction factors (e.g., NP size and zeta potential). Integration of the model, laboratory, and field data suggests that Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures. In contrast, the risks of oxidative stress are low in Africa because of high night temperatures. According to the prediction, Africa is a suitable area for nanoenabled agriculture. The regional differences and temperature changes make nanoenabled agriculture complicated. In the future, the temperature increase may reduce the oxidative stress in African bean and European maize induced by NPs. This study projects the development potential of nanoenabled agriculture using machine learning, although many more field studies are needed to address the differences at the country and continental scales

Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis

<p>Abstract</p> <p>Background</p> <p>Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public.</p> <p>Results</p> <p>In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile.</p> <p>Conclusion</p> <p>Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software based on the existing infrastructure.</p

Fuzzy-Based Optimal Adaptive Line-of-Sight Path Following for Underactuated Unmanned Surface Vehicle with Uncertainties and Time-Varying Disturbances

This paper investigates the path following control problem for an underactuated unmanned surface vehicle (USV) in the presence of dynamical uncertainties and time-varying external disturbances. Based on fuzzy optimization algorithm, an improved adaptive line-of-sight (ALOS) guidance law is proposed, which is suitable for straight-line and curve paths. On the basis of guidance information provided by LOS, a three-degree-of-freedom (DOF) dynamic model of an underactuated USV has been used to design a practical path following controller. The controller is designed by combining backstepping method, neural shunting model, neural network minimum parameter learning method, and Nussbaum function. Neural shunting model is used to solve the problem of “explosion of complexity,” which is an inherent illness of backstepping algorithm. Meanwhile, a simpler neural network minimum parameter learning method than multilayer neural network is employed to identify the uncertainties and time-varying external disturbances. In particular, Nussbaum function is introduced into the controller design to solve the problem of unknown control gain coefficient. And much effort is made to obtain the stability for the closed-loop control system, using the Lyapunov stability theory. Simulation experiments demonstrate the effectiveness and reliability of the improved LOS guidance algorithm and the path following controller

Baseline Demographic and Clinical Characteristics of Patients with Adrenal Incidentaloma from a Single Center in China: A Survey

Aim. To investigate the clinical and endocrinological characteristics of patients with adrenal incidentaloma (AI). Materials and Methods. This retrospective study enrolled 1941 AI patients hospitalized at the Department of Endocrinology, Chinese PLA General Hospital, Beijing, China, between January 1997 and December 2016. The patient gender, age at visits, imaging features, functional status, and histological results were analyzed. Results. Of the 1941 patients, 984 (50.70%) were men. The median age was 52 years (interquartile range: 44–69 years). 140 cases had bilateral AI. Endocrine evaluation showed that 1411 (72.69%) patients had nonfunctional tumor, 152 (7.83%) had subclinical Cushing syndrome (SCS), and 82 (4.33%) had primary hyperaldosteronism. A total of 925 patients underwent operation for removal of 496 cortical adenomas (53.62%), 15 adrenal cortical carcinomas (1.62%), and 172 pheochromocytomas (18.59%). The bilateral group had a higher proportion of SCS (18.57% versus 7.10%, P<0.001, P=0.006). A mass size of 46 mm was of great value in distinguishing malignant tumors from the benign tumors, with sensitivity of 88.2% and specificity of 95.5%. Conclusions. We reported the baseline demographic and clinical characteristics of patients with AI in a large series from a single center in China

MACROD1/LRP16 Enhances LPS-Stimulated Inflammatory Responses by Up-Regulating a Rac1-Dependent Pathway in Adipocytes

Background/Aims: Chronic inflammation contributes to the development of type 2 diabetes mellitus by targeting the insulin receptor substrate protein-1 (IRS-1) signaling pathway. Previous studies showed that Leukemia related protein 16 (LRP16) reduced insulin stimulated glucose uptake in adipocytes by impairing the IRS-1 signaling pathway. We explored the mechanism by which LRP16 promotes the inflammatory response. Methods: We screened LRP16 induced proteins in the lipopolysaccharide (LPS)-stimulated inflammatory response using liquid chromatography-mass spectrometry (LC-MS) and analyzed the potential biological functions of these proteins using online bioinformatics tools. mRNA expression and protein expression of target genes were measured by real time PCR and Western blot, respectively. Results: A total of 390 differentially expressed proteins were identified. The mitogen-activated protein kinase (MAPK) signaling pathway was the primary activated pathway in LRP16-expressing cells. Overexpression of LRP16 activated ERK1/2 and Rac1, which are two key players related to the MAPK signaling pathway. Furthermore, knock down of endogenous LRP16 by RNA interference (RNAi) reduced Rac1 expression, ERK activation, and inflammatory cytokine expression in human adipocytes stimulated by LPS. The stimulatory effect of LRP16 was diminished by suppressing Rac1 expression and treating the cells with the ERK specific inhibitor, PD98059. Conclusion: These findings revealed the functions of LRP16 in promoting the inflammatory response through activating the Rac1-MAPK1/ERK pathway in human adipocytes

Association of weight-adjusted-waist index with type 2 diabetes mellitus in Chinese urban adults: a cross-sectional study

BackgroundRecently, weight-adjusted-waist index (WWI), a new index for evaluating obesity, has been developed. This study aimed to examine the association between WWI and T2DM in Chinese urban adults.MethodA total of 5,0978 eligible participants drawn from the prospective REACTION study (Cancer Risk Assessment in Chinese People with Diabetes) were included in this study. Participants were divided into 3 groups based on baseline WWI levels. Pearson correlation analysis and binary logistic regression analysis were conducted to explore the association of WWI with T2DM risk factors and with T2DM risk.ResultsThe prevalence of obesity, central obesity and T2DM was 14.2%, 46.8% and 11.0% respectively, with a median age of 57 years. Logistic analysis showed that the WWI was significantly associated with the risk of T2DM. Compared to the lowest tertile of WWI (T1) serving as the reference group, the second tertile (T2) and the third tertile (T3) were associated with a 0.218-fold [1.218 (1.152, 1.288), P &lt;0.001] and 0.286-fold [1.286 (1.212, 1.364), P &lt;0.001] increase in the odds of developing T2DM respectively. After adjusting for all factors with the exception of the stratified variable, this association held true in age, sex, BMI, hypertension, and hyperlipidemia subgroup and was especially pronounced in those aged &lt;60 years, BMI ≥24 kg/m2, and males, with interactions between WWI and age, sex, and BMI (P for interaction &lt;0.05).ConclusionWWI was positively associated with T2DM in Chinese urban adults, especially in young and middle-aged males with BMI ≥24 kg/m2

The first compound heterozygous mutations in SLC12A3 and PDX1 genes: a unique presentation of Gitelman syndrome with distinct insulin resistance and familial diabetes insights

BackgroundGitelman Syndrome (GS) patients frequently exhibit disrupted glucose metabolism, attributed to hypokalemia, hypomagnesemia and heightened aldosterone. This study delved into the genetic underpinnings linked to insulin resistance and diabetes in a GS patient, contextualized within his family history.MethodsThe hydrochlorothiazide and furosemide loading test were performed to ascertain the presence of GS. Oral glucose tolerance test (OGTT) evaluated glucose metabolism and insulin sensitivity. Whole-exome sequencing, validated by Sanger sequencing, was employed to confirm gene mutations, which were then tracked among the patient’s relatives.ResultsSymptoms and laboratory examination confirmed the clinical diagnosis of GS. Comprehensive whole-exome sequencing, augmented by Sanger sequencing validation, revealed a compound heterozygous mutation within the SLC12A3 gene (c.1108G&gt;C in exon 9, c.676G&gt;A in exon 5 and c.2398G&gt;A in exon 20) in the patient. The OGTT affirmed diabetes and heightened insulin resistance, distinct from previous patients with GS we evaluated. Further genetic analysis identified a missense heterozygous mutation (c.97C&gt;G in exon 1) within the PDX1 gene, inherited from the patient’s diabetic mother without GS. Furthermore, the patient’s brother, with impaired glucose tolerance but regular potassium levels, also bore this mutation, hinting at additional impacts of the PDX1 gene mutation on glucose metabolism regulation beyond the known impacts of GS.ConclusionThis study unveils unprecedented compound heterozygous mutations in the SLC12A3 and PDX1 genes in a GS patient. These findings illuminate the potential complex genetic factors influencing glucose metabolism disruptions in GS.Take-home messageThis research uncovers a novel combination of SLC12A3 and PDX1 gene mutations in a Gitelman Syndrome patient, revealing intricate genetic factors that potentially disrupt glucose metabolism and shedding light on familial diabetes links

Characteristics of glucose metabolism indexes and continuous glucose monitoring system (CGMS) in patients with insulinoma

AIMS: Analyze the clinical applicability of glucose metabolism indexes and continuous glucose monitoring data on the qualitative diagnosis of insulinoma. METHODS: Involve 22 patients with insulinoma (insulinoma group), 11 patients with hypoglycemia (hypoglycemia group) and 31 people with normal glucose tolerance (control group). HbA1c, fasting blood glucose (FBG), insulin (FINS) and C-peptide (FCP) was tested. Using CGMS to monitor the blood glucose for three consecutive days and selecting the monitoring data of 24 h thereof, figuring out, with the aid of EasyGV Version 9.0, the mean glucose (MG), the standard deviation (SD) of blood glucose, CONGA (continuous overall net glycemic action), J-Index, LI (Lability Index), LBGI (Low Blood Glucose Index), HBGI (High Blood Glucose Index), GRADE (glycaemic risk assessment diabetes equation), MAGE (mean aplitude of glycaemic excursions), M value, MAG (mean absolute glucose). RESULTS: (1) FBG and LBG of insulinoma group are lower than those of control group and those of hypoglycemia group while FINS and FCP of insulinoma group are markedly higher than those of the other two groups; (2) the MG and CONGA of insulinoma group are lower than those of control group and its indexes like ST, LI, LBGI, GRADE, MAGE, M value and MAG are higher than those of control group; there are differences between the indexes of insulinoma group and those of hypoglycemia group in CONGA (lower than that of hypoglycemia group), LBGI (higher than that of hypoglycemia group), and M value (higher than that of hypoglycemia group). By drawing the ROC curve and calculating Youden index, the cut-off values of LBGI, M value, CONGA are respectively as 4.06, 7.79, 4.38, and the best index of differential diagnosis is LBGI. CONCLUSION: Continuous glucose monitoring data can be used to diagnose insulinoma and blood glucose fluctuation indicators such as LBGI, M value, CONGA might be useful to identify insulinoma