A Rapid and Low-Cost Pathogen Detection Platform by Using a Molecular Agglutination Assay.

Rapid and low-cost pathogen diagnostic approaches are critical for clinical decision-making procedures. Cultivating bacteria often takes days to identify pathogens and provide antimicrobial susceptibilities. The delay in diagnosis may result in compromised treatment and inappropriate antibiotic use. Over the past decades, molecular-based techniques have significantly shortened pathogen identification turnaround time with high accuracy. However, these assays often use complex fluorescent labeling and nucleic acid amplification processes, which limit their use in resource-limited settings. In this work, we demonstrate a wash-free molecular agglutination assay with a straightforward mixing and incubation step that significantly simplifies procedures of molecular testing. By targeting the 16S rRNA gene of pathogens, we perform a rapid pathogen identification within 30 min on a dark-field imaging microfluidic cytometry platform. The dark-field images with low background noise can be obtained using a narrow beam scanning technique with off-the-shelf complementary metal oxide semiconductor (CMOS) imagers such as smartphone cameras. We utilize a machine learning algorithm to deconvolute topological features of agglutinated clusters and thus quantify the abundance of bacteria. Consequently, we unambiguously distinguish Escherichia coli positive from other E. coli negative among 50 clinical urinary tract infection samples with 96% sensitivity and 100% specificity. Furthermore, we also apply this quantitative detection approach to achieve rapid antimicrobial susceptibility testing within 3 h. This work exhibits easy-to-use protocols, high sensitivity, and short turnaround time for point-of-care testing uses

Flavor SU(3) analysis of charmless B meson decays to two pseudoscalar mesons

Global fits to charmless B --> PP decays in the framework of flavor SU(3) symmetry are updated and improved without reference to the \sin2\beta measured from the charmonium decay modes. Fit results directly constrain the (\bar\rho,\bar\eta) vertex of the unitarity triangle, and are used to predict the branching ratios and CP asymmetries of all decay modes, including those of the B_s system. Different schemes of SU(3) breaking in decay amplitude sizes are analyzed. The major breaking effect between strangeness-conserving and strangeness-changing decays can be accounted for by including a ratio of decay constants in tree and color-suppressed amplitudes. The possibility of having a new physics contribution to K \pi decays is also examined from the data fitting point of view.Comment: 22 pages and 2 figures; some comments and references added; more references added, version to appear in journa

A Dyson Sphere around a black hole

The search for extraterrestrial intelligence (SETI) has been conducted for nearly 60 years. A Dyson Sphere, a spherical structure that surrounds a star and transports its radiative energy outward as an energy source for an advanced civilisation, is one of the main targets of SETI. In this study, we discuss whether building a Dyson Sphere around a black hole is effective. We consider six energy sources: (i) the cosmic microwave background, (ii) the Hawking radiation, (iii) an accretion disk, (iv) Bondi accretion, (v) a corona, and (vi) relativistic jets. To develop future civilisations (for example, a Type II civilisation), $4\times10^{26}\,{\rm W}$($1\,{\rm L_{\odot}}$) is expected to be needed. Among (iii) to (vi), the largest luminosity can be collected from an accretion disk, reaching $10^{5}\,{\rm L_{\odot}}$, enough to maintain a Type II civilisation. Moreover, if a Dyson Sphere collects not only the electromagnetic radiation but also other types of energy (e.g., kinetic energy) from the jets, the total collected energy would be approximately 5 times larger. Considering the emission from a Dyson Sphere, our results show that the Dyson Sphere around a stellar-mass black hole in the Milky Way ($10\,\rm kpc$ away from us) is detectable in the ultraviolet$(\rm 10-400\,{\rm nm)}$, optical$(\rm 400-760\,{\rm nm)}$, near-infrared($\rm 760\,{\rm nm}-5\,{\rm \mu m}$), and mid-infrared($\rm 5-40\,{\rm \mu m}$) wavelengths via the waste heat radiation using current telescopes such as Galaxy Evolution Explorer Ultraviolet Sky Surveys. Performing model fitting to observed spectral energy distributions and measuring the variability of radial velocity may help us to identify these possible artificial structures.Comment: This paper has been accepted for publication in MNRA

Acute-on-chronic kidney injury at hospital discharge is associated with long-term dialysis and mortality

Existing chronic kidney disease (CKD) is among the most potent predictors of postoperative acute kidney injury (AKI). Here we quantified this risk in a multicenter, observational study of 9425 patients who survived to hospital discharge after major surgery. CKD was defined as a baseline estimated glomerular filtration rate <45ml/min per 1.73m2. AKI was stratified according to the maximum simplified RIFLE classification at hospitalization and unresolved AKI defined as a persistent increase in serum creatinine of more than half above the baseline or the need for dialysis at discharge. A Cox proportional hazard model showed that patients with AKI-on-CKD during hospitalization had significantly worse long-term survival over a median follow-up of 4.8 years (hazard ratio, 3.3) than patients with AKI but without CKD. The incidence of long-term dialysis was 22.4 and 0.17 per 100 person-years among patients with and without existing CKD, respectively. The adjusted hazard ratio for long-term dialysis in patients with AKI-on-CKD was 19.8 compared to patients who developed AKI without existing CKD. Furthermore, AKI-on-CKD but without kidney recovery at discharge had a worse outcome (hazard ratios of 4.6 and 213, respectively) for mortality and long-term dialysis as compared to patients without CKD or AKI. Thus, in a large cohort of postoperative patients who developed AKI, those with existing CKD were at higher risk for long-term mortality and dialysis after hospital discharge than those without. These outcomes were significantly worse in those with unresolved AKI at discharge

Weighted-Support Vector Machine Learning Classifier of Circulating Cytokine Biomarkers to Predict Radiation-Induced Lung Fibrosis in Non-Small-Cell Lung Cancer Patients

Background: Radiation-induced lung fibrosis (RILF) is an important late toxicity in patients with non-small-cell lung cancer (NSCLC) after radiotherapy (RT). Clinically significant RILF can impact quality of life and/or cause non-cancer related death. This study aimed to determine whether pre-treatment plasma cytokine levels have a significant effect on the risk of RILF and investigate the abilities of machine learning algorithms for risk prediction. Methods: This is a secondary analysis of prospective studies from two academic cancer centers. The primary endpoint was grade≥2 (RILF2), classified according to a system consistent with the consensus recommendation of an expert panel of the AAPM task for normal tissue toxicity. Eligible patients must have at least 6 months' follow-up after radiotherapy commencement. Baseline levels of 30 cytokines, dosimetric, and clinical characteristics were analyzed. Support vector machine (SVM) algorithm was applied for model development. Data from one center was used for model training and development; and data of another center was applied as an independent external validation. Results: There were 57 and 37 eligible patients in training and validation datasets, with 14 and 16.2% RILF2, respectively. Of the 30 plasma cytokines evaluated, SVM identified baseline circulating CCL4 as the most significant cytokine associated with RILF2 risk in both datasets (P = 0.003 and 0.07, for training and test sets, respectively). An SVM classifier predictive of RILF2 was generated in Cohort 1 with CCL4, mean lung dose (MLD) and chemotherapy as key model features. This classifier was validated in Cohort 2 with accuracy of 0.757 and area under the curve (AUC) of 0.855. Conclusions: Using machine learning, this study constructed and validated a weighted-SVM classifier incorporating circulating CCL4 levels with significant dosimetric and clinical parameters which predicts RILF2 risk with a reasonable accuracy. Further study with larger sample size is needed to validate the role of CCL4, and this SVM classifier in RILF2

Automated PDF highlighting to support faster curation of literature for Parkinson's and Alzheimer's disease

Neurodegenerative disorders such as Parkinson’s and Alzheimer’s disease are devastating and costly illnesses, a source of major global burden. In order to provide successful interventions for patients and reduce costs, both causes and pathological processes need to be understood. The ApiNATOMY project aims to contribute to our understanding of neurodegenerative disorders by manually curating and abstracting data from the vast body of literature amassed on these illnesses. As curation is labour-intensive, we aimed to speed up the process by automatically highlighting those parts of the PDF document of primary importance to the curator. Using techniques similar to those of summarisation, we developed an algorithm that relies on linguistic, semantic and spatial features. Employing this algorithm on a test set manually corrected for tool imprecision, we achieved a macro F1-measure of 0.51, which is an increase of 132% compared to the best bag-of-words baseline model. A user based evaluation was also conducted to assess the usefulness of the methodology on 40 unseen publications, which reveals that in 85% of cases all highlighted sentences are relevant to the curation task and in about 65% of the cases, the highlights are sufficient to support the knowledge curation task without needing to consult the full text. In conclusion, we believe that these are promising results for a step in automating the recognition of curation-relevant sentences. Refining our approach to pre-digest papers will lead to faster processing and cost reduction in the curation process

Effect of changes in climate and emissions on future sulfate-nitrate-ammonium aerosol levels in the United States

Global simulations of sulfate, nitrate, and ammonium aerosols are performed for the present day and 2050 using the chemical transport model GEOS-Chem. Changes in climate and emissions projected by the IPCC A1B scenario are imposed separately and together, with the primary focus of the work on future inorganic aerosol levels over the United States. Climate change alone is predicted to lead to decreases in levels of sulfate and ammonium in the southeast U.S. but increases in the Midwest and northeast U.S. Nitrate concentrations are projected to decrease across the U.S. as a result of climate change alone. In the U.S., climate change alone can cause changes in annually averaged sulfate-nitrate-ammonium of up to 0.61 μg/m^3, with seasonal changes often being much larger in magnitude. When changes in anthropogenic emissions are considered (with or without changes in climate), domestic sulfate concentrations are projected to decrease because of sulfur dioxide emission reductions, and nitrate concentrations are predicted to generally increase because of higher ammonia emissions combined with decreases in sulfate despite reductions in emissions of nitrogen oxides. The ammonium burden is projected to increase from 0.24 to 0.36 Tg, and the sulfate burden to increase from 0.28 to 0.40 Tg S as a result of globally higher ammonia and sulfate emissions in the future. The global nitrate burden is predicted to remain essentially constant at 0.35 Tg, with changes in both emissions and climate as a result of the competing effects of higher precursor emissions and increased temperature

Environmental and lifestyle risk factors of breast cancer in Malta-a retrospective case-control study

The funding for this research was obtained as part of IMaGenX – and ItaliaMalta co-financed EU project Operational Programme 2007–2013.AIM AND BACKGROUND: Environmental exposures are known to play a role in the development of cancer, including breast cancer. There are known associations of breast cancer with environmental factors such as sunlight exposure, diet and exercise and alcohol consumption as well as physiological factors. This study examines the prevalence of risk factors for breast cancer related to dietary intake, environment and lifestyle in the female population of Malta. Malta has had little research in this area, and therefore an exploratory study was carried out. METHODS: A retrospective case-control design was applied. Two hundred cases and 403 controls were included. Both cases and controls were subjects without a known family history for breast cancer. Controls were age-matched to cases in an age-decade category roughly at a 2:1 ratio. Interviews were carried out face-to-face using a questionnaire designed by Maltese and Sicilian researchers, encompassing various factors including diet, lifestyle, physiological factors and medical history. Breast cancer risk was then analysed using both univariate and multivariate analyses. For factors having a metric scale, the Mann-Whitney test was used to compare mean scores, while for categorical factors, the chi-square test was used to compare percentages between the case and control groups. Statistical modelling was carried out using binary logistic regression to relate the likelihood of breast cancer to over 50 risk/protective factors analysed collectively. RESULTS: Univariate analysis showed around 20 parameters of interest, 14 of which were statistically significant at a 0.05 level of significance. Logistic regression analysis identified 11 predictors of interest that were statistically significant. Tomato, coffee and canned meat consumption were associated with lower likelihood of breast cancer (OR = 0.988, 0.901, 0.892, respectively), whereas beans and cabbage consumption and low sodium salt were positively associated with breast cancer (OR = 1.045, 1.834, 1.028, respectively). Premenopausal status was associated with a lower risk of breast cancer compared to postmenopausal status (OR = 0.067). Not having experienced myocardial infarction was associated with lower odds of breast cancer (OR = 0.331). Increased height was also found to have a strong association with risk of breast cancer, with the odds of having breast cancer increasing for every centimetre increase in height (OR = 1.048). In terms of quantity, odds of having breast cancer were lower in those exposed to sunlight (OR = 0.891). The odds of having breast cancer were also lower in those not using the oral contraceptive pill (OR = 0.454). CONCLUSIONS: Various factors in this exploratory study were found to be associated with development of breast cancer. While causal conclusions cannot be made, tomato consumption is of particular interest, as these results corroborate findings found in other studies. A negative association of breast cancer with sunlight exposure and oral contraceptive pill use corroborates findings in other studies. Other associations with dietary intake can be explained by dietary changes. More robust studies in this area, including possible longitudinal studies, are warranted.peer-reviewe

A multi-targeted approach to suppress tumor-promoting inflammation

Cancers harbor significant genetic heterogeneity and patterns of relapse following many therapies are due to evolved resistance to treatment. While efforts have been made to combine targeted therapies, significant levels of toxicity have stymied efforts to effectively treat cancer with multi-drug combinations using currently approved therapeutics. We discuss the relationship between tumor-promoting inflammation and cancer as part of a larger effort to develop a broad-spectrum therapeutic approach aimed at a wide range of targets to address this heterogeneity. Specifically, macrophage migration inhibitory factor, cyclooxygenase-2, transcription factor nuclear factor-κB, tumor necrosis factor alpha, inducible nitric oxide synthase, protein kinase B, and CXC chemokines are reviewed as important antiinflammatory targets while curcumin, resveratrol, epigallocatechin gallate, genistein, lycopene, and anthocyanins are reviewed as low-cost, low toxicity means by which these targets might all be reached simultaneously. Future translational work will need to assess the resulting synergies of rationally designed antiinflammatory mixtures (employing low-toxicity constituents), and then combine this with similar approaches targeting the most important pathways across the range of cancer hallmark phenotypes