Petersdamm5255

The outcome of this research may pave the way for developing the next generation in vitro and in vivo drug capture additives and devices.Toxicology in the 21st Century has seen a shift from chemical risk assessment based on traditional animal tests, identifying apical endpoints and doses that are "safe", to the prospect of Next Generation Risk Assessment based on non-animal methods. Increasingly, large and high throughput in vitro datasets are being generated and exploited to develop computational models. This is accompanied by an increased use of machine learning approaches in the model building process. A potential problem, however, is that such models, while robust and predictive, may still lack credibility from the perspective of the end-user. In this commentary, we argue that the science of causal inference and reasoning, as proposed by Judea Pearl, will facilitate the development, use and acceptance of quantitative AOP models. Our hope is that by importing established concepts of causality from outside the field of toxicology, we can be "constructively disruptive" to the current toxicological paradigm, using the "Causal Revolution" to bring about a "Toxicological Revolution" more rapidly.

The study aimed to identify yet unknown and uncharacterized long non-coding RNAs (lncRNAs) in treatment-naïve ulcerative colitis (UC), and to define their possible roles in UC pathogenesis. For that purpose, accurate quantification methods for lncRNA transcript detection, multiple and "stringent" strategies were applied. New insights in the regulation of functional genes and pathways of relevance for UC through expression of lncRNAs are expected.

The study was based on sequencing data derived from a data set consisting of treatment-naïve UC patients (n=14) and control subjects (n=16). Two complementary aligners were used to identify lncRNAs. Several different steps were used to validate differential expression including plotting the reads over the annotation for manual inspection. To help determine potential lncRNA involvement in biological processes, KEGG pathway enrichment was done on protein-coding genes which co-expressed with the lncRNAs.

A total of 99 lncRNAs were identified in UC. The lncRNAs which were not previously characterized (n=15) in UC or other autoimmune diseases were selected for down-stream analysis. In total, 602 protein-coding genes correlated with the uncharacterized lncRNAs. KEGG pathway enrichment analysis revealed involvement of lncRNAs in two significantly enriched pathways, lipid and atherosclerosis, and T-cell receptor signaling.

This study identified a set of 15 yet uncharacterized lncRNAs which may be of importance for UC pathogenesis. These lncRNAs may serve as potential diagnostic biomarkers and might be of use for the development of UC treatment strategies in the future.

This study identified a set of 15 yet uncharacterized lncRNAs which may be of importance for UC pathogenesis. These lncRNAs may serve as potential diagnostic biomarkers and might be of use for the development of UC treatment strategies in the future.Cirrhosis and hepatocellular carcinoma (HCC) are related to chronic liver diseases. Diagnostic algorithms are needed to discriminate HCC from cirrhosis for better patient management. This study aimed to determine the potential of miR-122 and miR-150 to differentiate HCC from liver cirrhosis. This study used a cross-sectional method involving 66 patients with liver cirrhosis, 27 subjects with HCC, and 29 healthy controls. Examination of miR-122 and miR-150 levels from blood plasma used real-time quantitative polymerase chain reaction and their relative expressions were calculated. Clinical and laboratory data were collected and graphed for the Area Under the Curve (AUC) and also for comparison using unpaired T-tests, Kruskal-Wallis, Mann-Whitney, and Chi-square tests with significance set as p less then 0.05. The relative expressions of miR-122 and miR-150 could differentiate HCC from cirrhosis, with cut-off 9.11, AUC 53.84%, p = 0.2120, and cut-off 1.47, AUC 67.65%, p = 0.0001, respectively. Meanwhile, the combined relative expressions of miR-122 and miR-150 can distinguish HCC from cirrhosis, with AUC 71.94%, p = 0.0006. The combination of miR-122 and miR-150 has the potential as a biomarker to differentiate HCC from liver cirrhosis.In the field of landscape epidemiology, the contribution of machine learning (ML) to modeling of epidemiological risk scenarios presents itself as a good alternative. This study aims to break with the "black box" paradigm that underlies the application of automatic learning techniques by using SHAP to determine the contribution of each variable in ML models applied to geospatial health, using the prevalence of hookworms, intestinal parasites, in Ethiopia, where they are widely distributed; the country bears the third-highest burden of hookworm in Sub-Saharan Africa. XGBoost software was used, a very popular ML model, to fit and analyze the data. The Python SHAP library was used to understand the importance in the trained model, of the variables for predictions. The description of the contribution of these variables on a particular prediction was obtained, using different types of plot methods. The results show that the ML models are superior to the classical statistical models; not only demonstrating similar results but also explaining, by using the SHAP package, the influence and interactions between the variables in the generated models. This analysis provides information to help understand the epidemiological problem presented and provides a tool for similar studies.

Noticing health warnings on cigarette packages has been associated with thinking about quitting. see more This study examined sociodemographic characteristics associated with awareness of health warnings on cigarette packages and thinking about quitting because of health warning labels among adults who currently smoked tobacco.

We analyzed data from the 2017 Zambia WHO STEPS survey (STEPwise approach to surveillance) for noncommunicable disease risk factors. Descriptive analyses and logistic regression were performed to assess the association of select sociodemographic characteristics with awareness of health warnings and thinking about quitting because of health warnings.

Adults who currently smoked tobacco who were aged 30-44 years, of Chewa ethnicity, or with a formal education, were more likely to be aware of health warnings than those aged 18-29 years (adjusted prevalence ratio, APR=1.26; 95% CI 1.02-1.54), of Bemba ethnicity (APR=1.43; 95% CI 1.17-1.74), or with no formal education (APR 2.61-5.95). Among ary.

Sociodemographic characteristics such as sex, ethnicity, and education level were significantly associated with awareness of cigarette health warnings. Among cigarette smokers aware of health warnings, no sociodemographic differences in thinking about quitting were found. Tobacco control campaigns may need to target people of ethnicities with the highest smoking prevalence in the country.In multicellular organisms, metabolism is compartmentalized at many levels, including tissues and organs, different cell types, and subcellular compartments. Compartmentalization creates a coordinated homeostatic system where each compartment contributes to the production of energy and biomolecules the organism needs to carrying out specific metabolic tasks. Experimentally studying metabolic compartmentalization and metabolic interactions between cells and tissues in multicellular organisms is challenging at a systems level. However, recent progress in computational modeling provides an alternative approach to this problem. Here we discuss how integrating metabolic network modeling with omics data offers an opportunity to reveal metabolic states at the level of organs, tissues and, ultimately, individual cells. We review the current status of genome-scale metabolic network models in multicellular organisms, methods to study metabolic compartmentalization in silico, and insights gained from computational analyses. We also discuss outstanding challenges and provide perspectives for the future directions of the field.Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI), such as Erlotinib, have demonstrated remarkable efficacy in the treatment of non-small cell lung cancer (NSCLC) patients with mutated EGFR. However, the efficacy of EGFR-TKIs in wild-type (wt) EGFR tumours has been shown to be marginal. Methods that can sensitize Erlotinib to EGFR wild-type NSCLC remain rare. Herein, we developed a multifunctional superparamagnetic nanotheranostic agent as a novel strategy to potentiate Erlotinib to EGFR-wt NSCLCs. Our results demonstrate that the nanoparticles can co-escort Erlotinib and a vascular epithermal growth factor (VEGF) inhibitor, Bevacizumab (Bev), to EGFR-wt tumours. The nanotheranostic agent exhibits remarkable effects as an inhibitor of EGFR-wt tumour growth. Moreover, Bev normalizes the tumour embedded vessels, further promoting the therapeutic efficacy of Erlotinib. In addition, the tumour engagement of the nanoparticles and the vascular normalization could be tracked by magnetic resonance imaging (MRI). Collectively, our study, for the first time, demonstrated that elaborated nanoparticles could be employed as a robust tool to potentiate Erlotinib to EGFR-wt NSCLC, paving the way for imaging-guided nanotheranostics for refractory NSCLCs expressing EGFR wild-type genes.A moderate inflammatory response at the early stages of fracture healing is necessary for callus formation. Over-active and continuous inflammation, however, impairs fracture healing and leads to excessive tissue damage. Adequate fracture healing could be promoted through suppression of local over-active immune cells in the fracture site. In the present study, we achieved an enriched concentration of PD-L1 from exosomes (Exos) of a genetically engineered Human Umbilical Vein Endothelial Cell (HUVECs), and demonstrated that exosomes overexpressing PD-L1 specifically bind to PD-1 on the T cell surface, suppressing the activation of T cells. Furthermore, exosomal PD-L1 induced Mesenchymal Stem Cells (MSCs) towards osteogenic differentiation when pre-cultured with T cells. Moreover, embedding of Exos into an injectable hydrogel allowed Exos delivery to the surrounding microenvironment in a time-released manner. Additionally, exosomal PD-L1, embedded in a hydrogel, markedly promoted callus formation and fracture healing in a murine model at the early over-active inflammation phase. Importantly, our results suggested that activation of T cells in the peripheral lymphatic tissues was inhibited after local administration of PD-L1-enriched Exos to the fracture sites, while T cells in distant immune organs such as the spleen were not affected. In summary, this study provides the first example of using PD-L1-enriched Exos for bone fracture repair, and highlights the potential of Hydrogel@Exos systems for bone fracture therapy through immune inhibitory effects.Glioma is one of the most malignant primary tumors affecting the brain. The efficacy of therapeutics for glioma is seriously compromised by the restriction of blood-brain barrier (BBB), interstitial tumor pressure of resistance to chemotherapy/radiation, and the inevitable damage to normal brain tissues. Inspired by the natural structure and properties of high-density lipoprotein (HDL), a tumor-penetrating lipoprotein was prepared by the fusion tLyP-1 to apolipoprotein A-I-mimicking peptides (D4F), together with indocyanine green (ICG) incorporation and lipophilic small interfering RNA targeted HIF-1α (siHIF) surface anchor for site-specific photo-gene therapy. tLyP-1 peptide is fused to HDL-surface to facilitate BBB permeability, tumor-homing capacity and -site accumulation of photosensitizer and siRNA. Upon NIR light irradiation, ICG not only served as real-time targeted imaging agent, but also provided toxic reactive oxygen species and local hyperthermia for glioma phototherapy. The HIF-1α siRNA in this nanoplatform downregulated the hypoxia-induced HIF-1α level in tumor microenvironment and enhanced the photodynamic therapy against glioma.