Gold nanoparticles and radiofrequency in experimental models for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal and chemo-refractory cancers, clearly, alternative treatment strategies are needed. We utilized 10 nm gold nanoparticles as a scaffold to synthesize nanoconjugates bearing a targeting antibody (cetuximab, C225) and gemcitabine. Loading efficiency of gemcitabine on the gold nanoconjugates was 30%. Targeted gold nanoconjugates in combination with RF were selectively cytotoxic to EGFR expressing Hep3B and SNU449 cells when compared to isotype particles with/without RF (P < 0.05). In animal experiments, targeted gold nanoconjugates halted the growth of subcutaneous Hep3B xenografts in combination with RF exposure (P < 0.05). These xenografts also demonstrated increased apoptosis, necrosis and decreased proliferation compared to controls. Normal tissues were unharmed. We have demonstrated that non-invasive RF-induced hyperthermia when combined with targeted delivery of gemcitabine is more effective and safe at dosages ~ 275-fold lower than the current clinically-delivered systemic dose of gemcitabine

Pressure-induced collapsed-tetragonal phase in SrCo2As2

We present high-energy x-ray diffraction data under applied pressures up to p = 29 GPa, neutron diffraction measurements up to p = 1.1 GPa, and electrical resistance measurements up to p = 5.9 GPa, on SrCo2As2. Our x-ray diffraction data demonstrate that there is a first-order transition between the tetragonal (T) and collapsed-tetragonal (cT) phases, with an onset above approximately 6 GPa at T = 7 K. The pressure for the onset of the cT phase and the range of coexistence between the T and cT phases appears to be nearly temperature independent. The compressibility along the a-axis is the same for the T and cT phases whereas, along the c-axis, the cT phase is significantly stiffer, which may be due to the formation of an As-As bond in the cT phase. Our resistivity measurements found no evidence of superconductivity in SrCo2As2 for p <= 5.9 GPa and T >= 1.8 K. The resistivity data also show signatures consistent with a pressure-induced phase transition for p >= 5.5 GPa. Single-crystal neutron diffraction measurements performed up to 1.1 GPa in the T phase found no evidence of stripe-type or A-type antiferromagnetic ordering down to 10 K. Spin-polarized total-energy calculations demonstrate that the cT phase is the stable phase at high pressure with a c/a ratio of 2.54. Furthermore, these calculations indicate that the cT phase of SrCo2As2 should manifest either A-type antiferromagnetic or ferromagnetic order.Comment: 6 pages, 5 figure

Smart Adaptive Homes and Their Potential to Improve Space Efficiency and Personalisation

Over the last decades, population growth in urban areas and the subsequent rise in demand for housing have resulted in significant space and housing shortages. This paper investigates the influence of smart technologies on small urban dwellings to make them flexible, adaptive and personalised. The study builds on the hypothesis that adaptive homes and smart technology could increase efficiency and space usage up to two to three times compared to a conventional apartment. The present study encompasses a comprehensive semi-systematic literature review that includes several case studies of smart adaptive homes demonstrating various strategies that can be employed to enhance the functionality of small spaces while reducing the physical and psychological limitations associated with them. These strategies involve incorporating time-dependent functions and furniture, as well as division elements that can adapt to the changing needs of users in real-time. This review further categorises types of flexibility and adaptation regarding the size of the moving elements, the time that the transformation takes and whether it is performed manually (by a human) or automatically (by a machine). Results show that smart and adaptive technology can increase space efficiency by reducing the need for separate physical spaces for different activities. Smart technology substantially increases the versatility and multifunctionality of a room in all three dimensions and allows for adaptation and customisation for a variety of users

Systematic Review of Medicine-Related Problems in Adult Patients with Atrial Fibrillation on Direct Oral Anticoagulants

New oral anticoagulant agents continue to emerge on the market and their safety requires assessment to provide evidence of their suitability for clinical use. There-fore, we searched standard databases to summarize the English language literature on medicine-related problems (MRPs) of direct oral anticoagulants DOACs (dabigtran, rivaroxban, apixban, and edoxban) in the treatment of adults with atri-al fibrillation. Electronic databases including Medline, Embase, International Pharmaceutical Abstract (IPA), Scopus, CINAHL, the Web of Science and Cochrane were searched from 2008 through 2016 for original articles. Studies pub-lished in English reporting MRPs of DOACs in adult patients with AF were in-cluded. Seventeen studies were identified using standardized protocols, and two reviewers serially abstracted data from each article. Most articles were inconclusive on major safety end points including major bleeding. Data on major safety end points were combined with efficacy. Most studies inconsistently reported adverse drug reactions and not adverse events or medication error, and no definitions were consistent across studies. Some harmful drug effects were not assessed in studies and may have been overlooked. Little evidence is provided on MRPs of DOACs in patients with AF and, therefore, further studies are needed to establish the safety of DOACs in real-life clinical practice

Influencing Factors on Student’s Willingness to Embrace Cloud Computing: An Empirical Study in Sri Lanka

Cloud computing is an important factor in the realm of information technology; however, its adoption by individual users and students remains insufficient. This paper examines the elements that affect students’ intention to use cloud services, addressing a gap in the literature that predominantly focuses on business customers. A structured questionnaire was given to 347 respondents in order to test a model based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Descriptive analysis, principal component analysis, and Structural Equation Modeling (SEM) were employed to infer the results. The findings indicate that perceived usefulness, perceived behavioral control, subjective norms, and attitudes toward cloud computing all exert a direct influence on students' intention to utilize cloud computing. It was found that behavioral intention was indirectly influenced by perceived ease of use, perceived transferability of computer skills, and trust in cloud computing providers. Additionally, perceived risk was directly affected by concerns related to vendor lock-in and security. Attitudes toward cloud computing were directly shaped by perceived usefulness, which, in turn, was indirectly influenced by ease of use, transferability of computer skills, and trust in providers. Furthermore, the results suggest that perceived usefulness acted as a fully mediating variable, whereas attitudes toward cloud computing served as only a partial mediator. As policy implications, the paper suggests that academic policies should enhance students' intention to adopt cloud services by emphasizing the practical benefits of cloud computing, enhancing user-friendliness, promoting digital literacy, ensuring data security and transparency, and fostering positive attitudes toward cloud technologies. DOI: http://doi.org/10.31357/fhss/vjhss.v10i01.0

Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets.

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S

Association of untargeted urinary metabolomics and lung cancer risk among never-smoking women in China

Importance Chinese women have the highest rate of lung cancer among female never-smokers in the world, and the etiology is poorly understood. Objective To assess the association between metabolomics and lung cancer risk among never-smoking women. Design, Setting, and Participants This nested case-control study included 275 never-smoking female patients with lung cancer and 289 never-smoking cancer-free control participants from the prospective Shanghai Women’s Health Study recruited from December 28, 1996, to May 23, 2000. Validated food frequency questionnaires were used for the collection of dietary information. Metabolomic analysis was conducted from November 13, 2015, to January 6, 2016. Data analysis was conducted from January 6, 2016, to November 29, 2018. Exposures Untargeted ultra-high-performance liquid chromatography–tandem mass spectrometry and nuclear magnetic resonance metabolomic profiles were characterized using prediagnosis urine samples. A total of 39 416 metabolites were measured. Main Outcomes and Measures Incident lung cancer. Results Among the 564 women, those who developed lung cancer (275 participants; median [interquartile range] age, 61.0 [52-65] years) and those who did not develop lung cancer (289 participants; median [interquartile range] age, 62.0 [53-66] years) at follow-up (median [interquartile range] follow-up, 10.9 [9.0-11.7] years) were similar in terms of their secondhand smoke exposure, history of respiratory diseases, and body mass index. A peak metabolite, identified as 5-methyl-2-furoic acid, was significantly associated with lower lung cancer risk (odds ratio, 0.57 [95% CI, 0.46-0.72]; P < .001; false discovery rate = 0.039). Furthermore, this peak was weakly correlated with self-reported dietary soy intake (ρ = 0.21; P < .001). Increasing tertiles of this metabolite were associated with lower lung cancer risk (in comparison with first tertile, odds ratio for second tertile, 0.52 [95% CI, 0.34-0.80]; and odds ratio for third tertile, 0.46 [95% CI, 0.30-0.70]), and the association was consistent across different histological subtypes and follow-up times. Additionally, metabolic pathway analysis found several systemic biological alterations that were associated with lung cancer risk, including 1-carbon metabolism, nucleotide metabolism, oxidative stress, and inflammation. Conclusions and Relevance This prospective study of the untargeted urinary metabolome and lung cancer among never-smoking women in China provides support for the hypothesis that soy-based metabolites are associated with lower lung cancer risk in never-smoking women and suggests that biological processes linked to air pollution may be associated with higher lung cancer risk in this population

Metabolome-wide association study on ABCA7 indicates a role of ceramide metabolism in Alzheimer's disease.

Genome-wide association studies (GWASs) have identified genetic loci associated with the risk of Alzheimer's disease (AD), but the molecular mechanisms by which they confer risk are largely unknown. We conducted a metabolome-wide association study (MWAS) of AD-associated loci from GWASs using untargeted metabolic profiling (metabolomics) by ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). We identified an association of lactosylceramides (LacCer) with AD-related single-nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0 × 10-5 to 1.3 × 10-44). We showed that plasma LacCer concentrations are associated with cognitive performance and genetically modified levels of LacCer are associated with AD risk. We then showed that concentrations of sphingomyelins, ceramides, and hexosylceramides were altered in brain tissue from Abca7 knockout mice, compared with wild type (WT) (P = 0.049-1.4 × 10-5), but not in a mouse model of amyloidosis. Furthermore, activation of microglia increases intracellular concentrations of hexosylceramides in part through induction in the expression of sphingosine kinase, an enzyme with a high control coefficient for sphingolipid and ceramide synthesis. Our work suggests that the risk for AD arising from functional variations in ABCA7 is mediated at least in part through ceramides. Modulation of their metabolism or downstream signaling may offer new therapeutic opportunities for AD

Copper regulates cyclic-AMP-dependent lipolysis

Cell signaling relies extensively on dynamic pools of redox-inactive metal ions such as sodium, potassium, calcium and zinc, but their redox-active transition metal counterparts such as copper and iron have been studied primarily as static enzyme cofactors. Here we report that copper is an endogenous regulator of lipolysis, the breakdown of fat, which is an essential process in maintaining body weight and energy stores. Using a mouse model of genetic copper misregulation, in combination with pharmacological alterations in copper status and imaging studies in a 3T3-L1 white adipocyte model, we found that copper regulates lipolysis at the level of the second messenger, cyclic AMP (cAMP), by altering the activity of the cAMP-degrading phosphodiesterase PDE3B. Biochemical studies of the copper-PDE3B interaction establish copper-dependent inhibition of enzyme activity and identify a key conserved cysteine residue in a PDE3-specific loop that is essential for the observed copper-dependent lipolytic phenotype