Notes

Percolation of Ion-Irradiation-Induced Dysfunction within Complicated Oxide Connects.
Systemic therapy for brain metastases (BM) is quickly moving from conventional cytotoxic chemotherapy toward targeted therapies, that allow a disruption of driver molecular pathways. The discovery of actionable driver mutations has led to the development of an impressive number of tyrosine kinase inhibitors (TKIs), that target the epidermal growth factor receptor (EGFR) mutations, anaplastic-lymphoma-kinase (ALK) rearrangements, and other rare molecular alterations in patients bearing metastatic non-small cell lung cancer (NSCLC) in the brain, with remarkable results in terms of intracranial disease control and overall survival. Moreover, these drugs may delay the use of local therapies, such as stereotactic radiosurgery (SRS) or whole-brain radiotherapy (WBRT). New drugs with higher molecular specificity and ability to cross the CNS barriers (BBB, BTB and blood-CSF) are being developed. Two major issues are related to targeted therapies. First, the emergence of a resistance is a common event, and a deeper among solid tumors those subgroups of patients with a higher risk of relapsing into the brain and novel drugs, active on either neoplastic or normal cells of the microenvironment, that are cooperating in the invasion of brain tissue.The purpose of the present work was to evaluate performance in pulmonary nodule detection, reading times and patient doses for ultra-low dose computed tomography (ULD-CT), standard dose chest CT (SD-CT), and digital radiography (DR). Pulmonary nodules were simulated in an anthropomorphic lung phantom. Thirty cases, 18 with lesions (45 total lesions of 3-12 mm) and 12 without lesions were acquired for each imaging modality. Three radiologists interpreted the cases in a free-response study. Performance was assessed using the JAFROC figure-of-merit (FOM). Performance was not significantly different between ULD-CT and SD-CT (FOMs 0.787 vs 0.814; ΔFOM 0.03), but both CT techniques were superior to DR (FOM 0.541; ΔFOM 0.31 and 0.28). find more Overall, the CT modalities took longer time to interpret than DR. ULD chest CT may serve as an alternative to both SD-CT and conventional radiography, considerably reducing dose in the first case and improving diagnostic accuracy in the second.Lipid metabolism plays important roles not only in the structural basis and energy supply of healthy cells but also in the oncogenesis and progression of cancers. In this study, we investigated the prognostic value of lipid metabolism-related genes in papillary thyroid cancer (PTC). The recurrence predictive gene signature was developed and internally and externally validated based on PTC datasets including The Cancer Genome Atlas (TCGA) and GSE33630 datasets. Univariate, LASSO, and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. The expression profiles of prognostic genes were further determined by immunohistochemistry of tissue microarray using in-house cohorts, which enrolled 97 patients. Kaplan-Meier curve, time-dependent receiver operating characteristic curve, nomogram, and decision curve analyses were used to assess the performance of the gene signature. We identified four recurrence-related genes, PDZK1IP1, TMC3, LRP2 and KCNJ13, and established a four-gene signature recurrence risk model. The expression profiles of the four genes in the TCGA and in-house cohort indicated that stage T1/T2 PTC and locally advanced PTC exhibit notable associations not only with clinicopathological parameters but also with recurrence. Calibration analysis plots indicate the excellent predictive performance of the prognostic nomogram constructed based on the gene signature. Single-sample gene set enrichment analysis showed that high-risk cases exhibit changes in several important tumorigenesis-related pathways, such as the intestinal immune network and the p53 and Hedgehog signaling pathways. Our results indicate that lipid metabolism-related gene profiling represents a potential marker for prognosis and treatment decisions for PTC patients.Osteosarcoma (OS), the most common malignant bone tumor with high metastatic potential, frequently affects children and adolescents. Epidermal growth factor receptor (EGFR)-targeted tyrosine kinase inhibitors exhibit encouraging anti-tumor activity for patients with solid tumors, whereas their effects on OS remain controversial. In the present study, we aimed to elucidate the anti-tumor activity of gefitinib for OS, as well as to explore the underlying mechanisms. Gefitinib inhibits cell viability, tumor growth, cell migration, and invasion and promotes cell apoptosis and G1 cycle arrest in OS at a relatively high concentration via suppressing the PI3K/Akt and ERK pathways. However, gefitinib treatment results in the feedback activation of signal transducer and activator of transcription 3 (STAT3) induced by interleukin 6 (IL-6) secretion. Combined treatment with gefitinib and stattic, an inhibitor for STAT3 phosphorylation, engenders more evident inhibitory effects on cell proliferation, migration, and invasion and promotive effects on cell apoptosis and G1 phase arrest in OS, compared with the single exposure to gefitinib or stattic. Western blot analysis demonstrates that stattic treatment in gefitinib-treated OS abrogates the IL-6-induced STAT3 activation and subsequently further restrains the activities of EGFR, Akt, and ERK pathways in tumor cells. This study confirms that the EGFR inhibitor of gefitinib has moderate anti-tumor effects on OS through IL-6 secretion-mediated STAT3 activation. Additional administration of stattic in EGFR-targeted therapies may contribute to improve the efficacy for OS.
Adherence to medications is a significant element of self-care behaviors for patients with cardiovascular diseases (CVDs). Non-adherence to cardiovascular medications is the major risk for poor outcomes following any cardiac event. However, there is a lack of studies that addressed medication adherence among patients with CVDs attending outpatient clinics in Arabic countries, including Jordan. Thus, this study purposed to assess the psychosocial factors (e.g. depression, anxiety, stress, social support and self-esteem) and their correlation with adherence to medications among patients with CVDs attending outpatient clinics in Jordan.

A total of 395 Jordanian patients attending CVDs outpatient clinics at government, military and private healthcare facilities were recruited.

Our study findings showed that 31.4% of the patients reported complete adherence to their medications. The proportion of psychological reactions reported by the participants was 72.1% for depressive symptoms, 62.6% for anxiety and 50.1% for stress; 79.7% had moderate and normal social support, and 44% had low self-esteem. Depression, anxiety and stress had a significant negative correlation with adherence to medications; however, self-esteem had a significant positive relationship with adherence to medications. In addition, depression, anxiety and stress were the main predictors of adherence to medications.

Our findings might aid in paving the road for designing and developing strategies and interventions to increase adherence to medications and minimize these psychosocial problems among CVD patients in outpatient clinics.
Our findings might aid in paving the road for designing and developing strategies and interventions to increase adherence to medications and minimize these psychosocial problems among CVD patients in outpatient clinics.
This study was conducted among a convenience sample of Ajman University students in UAE between February 2018 and May 2019.

All undergraduate and master's degree students from the 1st to 5th year of medical and non-medical colleges. The survey was carried out by distributing a pre-designed, pre-structured questionnaire to the students during lectures. The questionnaires were primarily composed of three sections demographic characteristics, educational characteristics and sleep quality characteristics. The data were analyzed using STATA version 14.2. Logistic regression analysis was used to investigate the association between sleep characteristics, social media use and other significant risk factors. The P-values < 0.05 were considered to be statistically significant.

The vast majority of the study participants were social media users, and a considerable proportion suffered from poor sleep quality. A significantly increased risk of bad sleep quality and intermittent/anxious sleep patterns were observed among social media users.

Health policymakers should fully consider these factors in improving the sleep quality of university students.
Health policymakers should fully consider these factors in improving the sleep quality of university students.In synthetic biology, biological cells and processes are dismantled and reassembled to make novel systems that do useful things. Designs are encoded by deoxyribonucleic acid (DNA); DNA makes biological (bio-)parts; bioparts are combined to make devices; devices are built into biological systems. Computers are used at all stages of the Design-Build-Test-Learn cycle, from mathematical modelling through to the use of robots for the automation of assembly and experimentation. Synthetic biology applies engineering principles of standardisation, modularity, and abstraction, enabling fast prototyping and the ready exchange of designs between synthetic biologists working around the world. Like toy building blocks, compatible modular designs enable bioparts to be combined and optimised easily; biopart specifications are shared in open registries. Synthetic biology is made possible due to major advances in DNA sequencing and synthesis technologies, and through knowledge gleaned in the field of systems biology. Systems biology aims to understand biology across scales, from the molecular and cellular, up to tissues and organisms, and describes cells as complex information-processing systems. By contrast, synthetic biology seeks to design and build its own systems. Applications of synthetic biology are wide-ranging but include impacting healthcare to improve diagnosis and make better treatments for disease; it seeks to improve the environment by finding novel ways to clean up pollution, make industrial processes for chemical synthesis sustainable, and remove the need for damaging farming practices by making better fertilisers. Synthetic biology has the potential to change the way we live and help us to protect the future of our planet.
Although increasing COVID-19 vaccination rates is critical to end the pandemic, vaccination goals are far from being achieved. Political partisanship may be a risk factor for getting the COVID-19 vaccine. This study examines the association between the political partisanship and vaccination rate at the county-level and quantifies the differences between the Democratic and Republican parties.

Data are from CDC, the NY Times, and the US Census and American Community Survey. Linear regressions are used to test the relationships between the political partisanship and COVID-19 vaccination rate at the county level. The dependent variable is the cumulative COVID-19 vaccination rate each month between January and August, 2021 and the explanatory variables are the county political partisanship and interaction terms between political partisanship and time dummies during the study period.

Republican counties consistently had lower vaccination rates than Democratic counties, and the gap in vaccination rates between a typical Democratic and Republican county has steadily widened by month.
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