237 research outputs found
Evaluation of probiotic content of common complementary foods used in Mubi Metropolis, Adamawa State, Nigeria
Background: Probiotic bacteria are becoming increasingly important in the context of human nutrition based on the role they play in immunological, digestive and respiratory functions.
Objective: This study investigated the probiotic content and strengths of some complementary foods commonly used in Mubi Adamawa state, Nigeria.
Materials and Methods: Locally made cereal pastes (kamu) made from cereal grains sorghum, millet, and maize were purchased from the Mubi general market and coded as LSG, LMT and LMZ, respectively. Three most commonly used commercial complementary foods in Mubi metropolis were also purchased from Mubi market packed in cans of 450g each and coded as CC1, CC2, and CC3 respectively. The basic ingredients in each commercial product were recorded from the labels on the packages. De ManRogasa Agar was used to isolate the probiotic bacteria in all the samples using standard methods of AOAC (2000). Colony count and fungi identification were carried out.All analyses were done in triplicates.Data was analysed for means and standard deviation using Statistix 9, version 9.1(2012).
RESULT: Commercial complementary foods CC1 and CC2 had Lactobacillus species isolated with bacteria count of 7.5 x 102 and 8.7 x 102 Cfu / g respectively while CC3 had no bacterial specie isolated. Local complementary food LSG had no probiotic bacteria isolated while LMTand LMZ had Lactobacillus species isolated with bacteria count of 5.4 x 102 and 6.5 x 102 (Cfu/g) respectively. Commercial complementary foods CC1, CC2 and CC3 had no yeast isolated. Local complementary food LSG had the least yeast count of 1.01 x 103
(Cfu / g) of Saccharomycescerevasiae. LMT had 6.06 x 102Cfu /g and LMZ had the highest yeast count of 9.26 x 102(Cfu / g ) of Saccharomyces Cerevasiae.
CONCLUSION: Local complementary foods used in this study contained both probiotic bacteria (Lactobacillus species) and yeast (Saccharomyces Cerevasiae)
Flexible bronchoscopic management of benign tracheal stenosis: long term follow-up of 115 patients
<p>Abstract</p> <p>Background</p> <p>Management of benign tracheal stenosis (BTS) varies with the type and extent of the disease and influenced by the patient's age and general health status, hence we sought to investigate the long-term outcome of patients with BTS that underwent minimally invasive bronchoscopic treatment.</p> <p>Methods</p> <p>Patients with symptomatic BTS were treated with flexible bronchoscopy therapeutic modalities that included the following: balloon dilatation, laser photo-resection, self-expanding metal stent placement, and High-dose rate endobronchial brachytherapy used in cases of refractory stent-related granulation tissue formation.</p> <p>Results</p> <p>A total of 115 patients with BTS and various cardiac and respiratory co-morbidities with a mean age of 61 (range 40-88) were treated between January 2001 and January 2009. The underlining etiologies for BTS were post - endotracheal intubation (N = 76) post-tracheostomy (N = 30), Wegener's granulomatosis (N = 2), sarcoidosis (N = 2), amyloidosis (N = 2) and idiopathic BTS (N = 3). The modalities used were: balloon dilatation and laser treatment (N = 98). Stent was placed in 33 patients of whom 28 also underwent brachytherapy. Complications were minor and mostly included granulation tissue formation. The overall success rate was 87%. Over a median follow-up of 51 months (range 10-100 months), 30 patients (26%) died, mostly due to exacerbation of their underlying conditions.</p> <p>Conclusions</p> <p>BTS in elderly patients with co-morbidities can be safely and effectively treated by flexible bronchoscopic treatment modalities. The use of HDR brachytherapy to treat granulation tissue formation following successful airway restoration is promising.</p
Cardiovascular testing recovery in Latin America one year into the COVID-19 pandemic: An analysis of data from an international longitudinal survey.
The INCAPS COVID Investigators Group, listed by name in the Appendix, thank cardiology and imaging professional societies worldwide for their assistance in disseminating the survey to their memberships. These include alphabetically, but are not limited to, American Society of Nuclear Cardiology, Arab Society of Nuclear Medicine, Australasian Association of Nuclear Medicine Specialists, Australia-New Zealand Society of Nuclear Medicine, Belgian Society of Nuclear Medicine, Brazilian Nuclear Medicine Society, British Society of Cardiovascular Imaging, Conjoint Committee for the Recognition of Training in CT Coronary Angiography Australia and New Zealand, Consortium of Universities and Institutions in Japan, Danish Society of Cardiology, Gruppo Italiano Cardiologia Nucleare, Indonesian Society of Nuclear Medicine, Japanese Society of Nuclear Cardiology, Moscow Regional Department of Russian Nuclear Medicine Society, Philippine Society of Nuclear Medicine, Russian Society of Radiology, Sociedad Española de Medicina Nuclear e Imagen Molecular, Society of Cardiovascular Computed Tomography, and Thailand Society of Nuclear Medicine.Peer reviewe
Interpersonal violence: an important risk factor for disease and injury in South Africa
<p>Abstract</p> <p>Background</p> <p>Burden of disease estimates for South Africa have highlighted the particularly high rates of injuries related to interpersonal violence compared with other regions of the world, but these figures tell only part of the story. In addition to direct physical injury, violence survivors are at an increased risk of a wide range of psychological and behavioral problems. This study aimed to comprehensively quantify the excess disease burden attributable to exposure to interpersonal violence as a risk factor for disease and injury in South Africa.</p> <p>Methods</p> <p>The World Health Organization framework of interpersonal violence was adapted. Physical injury mortality and disability were categorically attributed to interpersonal violence. In addition, exposure to child sexual abuse and intimate partner violence, subcategories of interpersonal violence, were treated as risk factors for disease and injury using counterfactual estimation and comparative risk assessment methods. Adjustments were made to account for the combined exposure state of having experienced both child sexual abuse and intimate partner violence.</p> <p>Results</p> <p>Of the 17 risk factors included in the South African Comparative Risk Assessment study, interpersonal violence was the second leading cause of healthy years of life lost, after unsafe sex, accounting for 1.7 million disability-adjusted life years (DALYs) or 10.5% of all DALYs (95% uncertainty interval: 8.5%-12.5%) in 2000. In women, intimate partner violence accounted for 50% and child sexual abuse for 32% of the total attributable DALYs.</p> <p>Conclusions</p> <p>The implications of our findings are that estimates that include only the direct injury burden seriously underrepresent the full health impact of interpersonal violence. Violence is an important direct and indirect cause of health loss and should be recognized as a priority health problem as well as a human rights and social issue. This study highlights the difficulties in measuring the disease burden from interpersonal violence as a risk factor and the need to improve the epidemiological data on the prevalence and risks for the different forms of interpersonal violence to complete the picture. Given the extent of the burden, it is essential that innovative research be supported to identify social policy and other interventions that address both the individual and societal aspects of violence.</p
Excess risk of adverse pregnancy outcomes in women with porphyria: a population-based cohort study
The porphyrias comprise a heterogeneous group of rare, primarily hereditary, metabolic diseases caused by a partial deficiency in one of the eight enzymes involved in the heme biosynthesis. Our aim was to assess whether acute or cutaneous porphyria has been associated with excess risks of adverse pregnancy outcomes. A population-based cohort study was designed by record linkage between the Norwegian Porphyria Register, covering 70% of all known porphyria patients in Norway, and the Medical Birth Registry of Norway, based on all births in Norway during 1967â2006. The risks of the adverse pregnancy outcomes preeclampsia, delivery by caesarean section, low birth weight, premature delivery, small for gestational age (SGA), perinatal death, and congenital malformations were compared between porphyric mothers and the rest of the population. The 200 mothers with porphyria had 398 singletons during the study period, whereas the 1,100,391 mothers without porphyria had 2,275,317 singletons. First-time mothers with active acute porphyria had an excess risk of perinatal death [adjusted odds ratio (OR) 4.9, 95% confidence interval (CI) 1.5â16.0], as did mothers with the hereditable form of porphyria cutanea tarda (PCT) (3.0, 1.2â7.7). Sporadic PCT was associated with an excess risk of SGA [adjusted relative risk (RR) 2.0, 1.2â3.4], and for first-time mothers, low birth weight (adjusted OR 3.4, 1.2â10.0) and premature delivery (3.5, 1.2â10.5) in addition. The findings suggest women with porphyria should be monitored closely during pregnancy
Air quality and urban sustainable development: the application of machine learning tools
[EN] Air quality has an efect on a populationÂżs quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-GĂłmez, NI.; DĂaz-ArĂ©valo, JL.; LĂłpez JimĂ©nez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology. 18(4):1-18. https://doi.org/10.1007/s13762-020-02896-6S118184Al-Dabbous A, Kumar P, Khan A (2017) Prediction of airborne nanoparticles at roadside location using a feedâforward artificial neural network. Atmos Pollut Res 8:446â454. https://doi.org/10.1016/j.apr.2016.11.004AntanasijeviÄ D, Pocajt V, PovrenoviÄ D, RistiÄ M, PeriÄ-GrujiÄ A (2013) PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511â519. https://doi.org/10.1016/j.scitotenv.2012.10.110Brink H, Richards JW, Fetherolf M (2016) Real-world machine learning. Richards JW, Fetherolf M (eds) Manning Publications Co. Berkeley, CA. https://www.manning.com/books/real-world-machine-learning. Accessed 26 Apr 2020Cervone G, Franzese P, Ezber Y, Boybeyi Z (2008) Risk assessment of atmospheric emissions using machine learning. Nat Hazard Earth Syst 8:991â1000. https://doi.org/10.5194/nhess-8-991-2008Chen S, Kan G, Li J, Liang K, Hong Y (2018) Investigating Chinaâs urban air quality using big data, information theory, and machine learning. Pol J Environ Stud 27:565â578. https://doi.org/10.15244/pjoes/75159Corani (2005) Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecol Model 185:513â529. https://doi.org/10.1016/j.ecolmodel.2005.01.008Cruz C, GĂłmez A, RamĂrez L, Villalva A, Monge O, Varela J, Quiroz J, Duarte H (2017) Calidad del aire respecto de metales (Pb, Cd, Ni, Cu, Cr) y relaciĂłn con salud respiratoria: caso Sonora, MĂ©xico. Rev Int Contam Ambient 33:23â34. https://doi.org/10.20937/RICA.2017.33.esp02.02de Hoogh K, HĂ©ritier H, Stafoggia M, KĂŒnzli N, Kloog I (2018) Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland. Environ Pollut 233:1147â1154. https://doi.org/10.1016/j.envpol.2017.10.025Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in BogotĂĄ, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering. Atmos Pollut Res 9:912â922. https://doi.org/10.1016/j.apr.2018.02.006GarcĂa N, Combarro E, del Coz J, Montañes E (2013) A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study. Appl Math Comput 219:8923â8937. https://doi.org/10.1016/j.amc.2013.03.018Gibert K, SĂ nchez-MĂ rre M, Sevilla B (2012) Tools for environmental data mining and intelligent decision support. In iEMSs. Leipzig, Germany. http://www.iemss.org/society/index.php/iemss-2012-proceedings. Accessed 26 Nov 2018Gibert K, SĂ nchez-MarrĂš M, Izquierdo J (2016) A survey on pre-processing techniques: relevant issues in the context of environmental data mining. Ai Commun 29:627â663. https://doi.org/10.3233/AIC-160710Gounaridis D, Chorianopoulos I, Koukoulas S (2018) Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Appl Geogr 90:134â144. https://doi.org/10.1016/j.apgeog.2017.12.001Holloway J, Mengersen K (2018) Statistical machine learning methods and remote sensing for sustainable development goals: a review. Remote Sens 10:1â21. https://doi.org/10.3390/rs10091365Ifaei P, Karbassi A, Lee S, Yoo Ch (2017) A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data. Energy Convers Manag 153:257â277. https://doi.org/10.1016/j.enconman.2017.10.014Kadiyala A, Kumar A (2017a) Applications of R to evaluate environmental data science problems. Environ Prog Sustain 36:1358â1364. https://doi.org/10.1002/ep.12676Kadiyala A, Kumar A (2017b) Vector time series-based radial basis function neural network modeling of air quality inside a public transportation bus using available software. Environ Prog Sustain 36:4â10. https://doi.org/10.1002/ep.12523Karimian H, Li Q, Wu Ch, Qi Y, Mo Y, Chen G, Zhang X, Sachdeva S (2019) Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations. Aerosol Air Qual Res 19:1400â1410. https://doi.org/10.4209/aaqr.2018.12.0450Krzyzanowski M, Apte J, Bonjour S, Brauer M, Cohen A, PrĂŒss-Ustun A (2014) Air pollution in the mega-cities. Curr Environ Health Rep 1:185â191. https://doi.org/10.1007/s40572-014-0019-7LĂ€ssig K, Morik (2016) Computat sustainability. Springer, Berlin. https://doi.org/10.1007/978-3-319-31858-5Li Y, Wu Y-X, Zeng Z-X, Guo L (2006) Research on forecast model for sustainable development of economy-environment system based on PCA and SVM. In: Proceedings of the 2006 international conference on machine learning and cybernetics, vol 2006. IEEE, Dalian, China, pp 3590â3593. https://doi.org/10.1109/ICMLC.2006.258576Liu B-Ch, Binaykia A, Chang P-Ch, Tiwari M, Tsao Ch-Ch (2017) Urban air quality forecasting based on multi- dimensional collaborative support vector regression (SVR): a case study of Beijing-Tianjin-Shijiazhuang. PLoS ONE 12:1â17. https://doi.org/10.1371/journal.pone.0179763Lubell M, Feiock R, Handy S (2009) City adoption of environmentally sustainable policies in Californiaâs Central Valley. J Am Plan Assoc 75:293â308. https://doi.org/10.1080/01944360902952295Ma D, Zhang Z (2016) Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. J Hazard Mater 311:237â245. https://doi.org/10.1016/j.jhazmat.2016.03.022Madu C, Kuei N, Lee P (2017) Urban sustainability management: a deep learning perspective. Sustain Cities Soc 30:1â17. https://doi.org/10.1016/j.scs.2016.12.012Mellos K (1988) Theory of eco-development. In: Perspectives on ecology. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-19598-5_4Ni XY, Huang H, Du WP (2017) Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data. Atmos Environ 150:146â161. https://doi.org/10.1016/j.atmosenv.2016.11.054Oprea M, Dragomir E, Popescu M, Mihalache S (2016) Particulate matter air pollutants forecasting using inductive learning approach. Rev Chim 67:2075â2081Paas B, Stienen J, VorlĂ€nder M, Schneider Ch (2017) Modelling of urban near-road atmospheric PM concentrations using an artificial neural network approach with acoustic data input. Environments 4:1â25. https://doi.org/10.3390/environments4020026Pandey G, Zhang B, Jian L (2013) Predicting submicron air pollution indicators: a machine learning approach. Environ Sci Proc Impacts 15:996â1005. https://doi.org/10.1039/c3em30890aPeng H, Lima A, Teakles A, Jin J, Cannon A, Hsieh W (2017) Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Qual Atmos Health 10:195â211. https://doi.org/10.1007/s11869-016-0414-3PĂ©rez-OrtĂz M, de La Paz-MarĂn M, GutiĂ©rrez PA, HervĂĄs-MartĂnez C (2014) Classification of EU countriesâ progress towards sustainable development based on ordinal regression techniques. Knowl Based Syst 66:178â189. https://doi.org/10.1016/j.knosys.2014.04.041Phillis Y, Kouikoglou V, Verdugo C (2017) Urban sustainability assessment and ranking of cities. Comput Environ Urban 64:254â265. https://doi.org/10.1016/j.compenvurbsys.2017.03.002Saeed S, Hussain L, Awan I, Idris A (2017) Comparative analysis of different statistical methods for prediction of PM2.5 and PM10 concentrations in advance for several hours. Int J Comput Sci Netw Secur 17:45â52Sayegh A, Munir S, Habeebullah T (2014) Comparing the performance of statistical models for predicting PM10 concentrations. Aerosol Air Qual Res 14:653â665. https://doi.org/10.4209/aaqr.2013.07.0259Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16:2598â2606. https://doi.org/10.1109/JSEN.2016.2514378Sierra B (2006) Aprendizaje automĂĄtico conceptos bĂĄsicos y avanzados Aspectos prĂĄcticos utilizando el software Weka. Madrid Pearson Prentice Hall, MadridSingh K, Gupta S, Rai P (2013) Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos Environ 80:426â437. https://doi.org/10.1016/j.atmosenv.2013.08.023Song L, Pang S, Longley I, Olivares G, Sarrafzadeh A (2014) Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression. In: International joint conference on neural networks. IEEE, Beijing, pp 623â630. https://doi.org/10.1109/IJCNN.2014.6889521Souza R, Coelho G, da Silva A, Pozza S (2015) Using ensembles of artificial neural networks to improve PM10 forecasts. Chem Eng Trans 43:2161â2166. https://doi.org/10.3303/CET1543361SuĂĄrez A, GarcĂa PJ, Riesgo P, del Coz JJ, Iglesias-RodrĂguez FJ (2011) Application of an SVM-based regression model to the air quality study at local scale in the AvilĂ©s urban area (Spain). Math Comput Model 54:453â1466. https://doi.org/10.1016/j.mcm.2011.04.017Tamas W, Notton G, Paoli C, Nivet M, Voyant C (2016) Hybridization of air quality forecasting models using machine learning and clustering: an original approach to detect pollutant peaks. Aerosol Air Qual Res 16:405â416. https://doi.org/10.4209/aaqr.2015.03.0193Toumi O, Le Gallo J, Ben Rejeb J (2017) Assessment of Latin American sustainability. Renew Sustain Energy Rev 78:878â885. https://doi.org/10.1016/j.rser.2017.05.013Tzima F, Mitkas P, Voukantsis D, Karatzas K (2011) Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst Appl 38:5019â5027. https://doi.org/10.1016/j.eswa.2010.09.148United Nations, Department of Economic and Social Affairs (2019) World urbanization prospects The 2018 Revision. New York. https://doi.org/10.18356/b9e995fe-enWang B (2019) Applying machine-learning methods based on causality analysis to determine air quality in China. Pol J Environ Stud 28:3877â3885. https://doi.org/10.15244/pjoes/99639Wang X, Xiao Z (2017) Regional eco-efficiency prediction with support vector spatial dynamic MIDAS. J Clean Prod 161:165â177. https://doi.org/10.1016/j.jclepro.2017.05.077Wang W, Men C, Lu W (2008) Online prediction model based on support vector machine. Neurocomputing 71:550â558. https://doi.org/10.1016/j.neucom.2007.07.020WCED (1987) Report of the world commission on environment and development: our common future: report of the world commission on environment and development. WCED, Oslo. https://doi.org/10.1080/07488008808408783Weizhen H, Zhengqiang L, Yuhuan Z, Hua X, Ying Z, Kaitao L, Donghui L, Peng W, Yan M (2014) Using support vector regression to predict PM10 and PM2.5. In: IOP conference series: earth and environmental science, vol 17. IOP. https://doi.org/10.1088/1755-1315/17/1/012268WHO (2016) OMS | La OMS publica estimaciones nacionales sobre la exposiciĂłn a la contaminaciĂłn del aire y sus repercusiones para la salud. WHO. http://www.who.int/mediacentre/news/releases/2016/air-pollution-estimates/es/. Accesed 26 Nov 2018Yeganeh N, Shafie MP, Rashidi Y, Kamalan H (2012) Prediction of CO concentrations based on a hybrid partial least square and support vector machine model. Atmos Environ 55:357â365. https://doi.org/10.1016/j.atmosenv.2012.02.092Zalakeviciute R, Bastidas M, Buenaño A, Rybarczyk Y (2020) A traffic-based method to predict and map urban air quality. Appl Sci. https://doi.org/10.3390/app10062035Zeng L, Guo J, Wang B, Lv J, Wang Q (2019) Analyzing sustainability of Chinese coal cities using a decision tree modeling approach. Resour Policy 64:101501. https://doi.org/10.1016/j.resourpol.2019.101501Zhan Y, Luo Y, Deng X, Grieneisen M, Zhang M, Di B (2018) Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environ Pollut 233:464â473. https://doi.org/10.1016/j.envpol.2017.10.029Zhang Y, Huan Q (2006) Research on the evaluation of sustainable development in Cangzhou city based on neural-network-AHP. In: Proceedings of the fifth international conference on machine learning and cybernetics, vol 2006. pp 3144â3147. https://doi.org/10.1109/ICMLC.2006.258407Zhang Y, Shang W, Wu Y (2009) Research on sustainable development based on neural network. In: 2009 Chinese control and decision conference. IEEE, pp 3273â3276. https://doi.org/10.1109/CCDC.2009.5192476Zhou Y, Chang F-J, Chang L-Ch, Kao I-F, Wang YS (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134â145. https://doi.org/10.1016/j.jclepro.2018.10.24
Recommendations for implementing stereotactic radiotherapy in peripheral stage IA non-small cell lung cancer: report from the Quality Assurance Working Party of the randomised phase III ROSEL study
<p>Abstract</p> <p>Background</p> <p>A phase III multi-centre randomised trial (ROSEL) has been initiated to establish the role of stereotactic radiotherapy in patients with operable stage IA lung cancer. Due to rapid changes in radiotherapy technology and evolving techniques for image-guided delivery, guidelines had to be developed in order to ensure uniformity in implementation of stereotactic radiotherapy in this multi-centre study.</p> <p>Methods/Design</p> <p>A Quality Assurance Working Party was formed by radiation oncologists and clinical physicists from both academic as well as non-academic hospitals that had already implemented stereotactic radiotherapy for lung cancer. A literature survey was conducted and consensus meetings were held in which both the knowledge from the literature and clinical experience were pooled. In addition, a planning study was performed in 26 stage I patients, of which 22 were stage 1A, in order to develop and evaluate the planning guidelines. Plans were optimised according to parameters adopted from RTOG trials using both an algorithm with a simple homogeneity correction (Type A) and a more advanced algorithm (Type B). Dose conformity requirements were then formulated based on these results.</p> <p>Conclusion</p> <p>Based on current literature and expert experience, guidelines were formulated for this phase III study of stereotactic radiotherapy versus surgery. These guidelines can serve to facilitate the design of future multi-centre clinical trials of stereotactic radiotherapy in other patient groups and aid a more uniform implementation of this technique outside clinical trials.</p
Improved functionalization of oleic acid-coated iron oxide nanoparticles for biomedical applications
Superparamagnetic iron oxide nanoparticles
can providemultiple benefits for biomedical applications
in aqueous environments such asmagnetic separation or
magnetic resonance imaging. To increase the colloidal
stability and allow subsequent reactions, the introduction
of hydrophilic functional groups onto the particlesâ
surface is essential. During this process, the original
coating is exchanged by preferably covalently bonded
ligands such as trialkoxysilanes. The duration of the
silane exchange reaction, which commonly takes more
than 24 h, is an important drawback for this approach. In
this paper, we present a novel method, which introduces
ultrasonication as an energy source to dramatically
accelerate this process, resulting in high-quality waterdispersible nanoparticles around 10 nmin size. To prove
the generic character, different functional groups were
introduced on the surface including polyethylene glycol
chains, carboxylic acid, amine, and thiol groups. Their
colloidal stability in various aqueous buffer solutions as
well as human plasma and serum was investigated to
allow implementation in biomedical and sensing
applications.status: publishe
COVAD survey 2 long-term outcomes: unmet need and protocol
Vaccine hesitancy is considered a major barrier to achieving herd immunity against COVID-19. While multiple alternative and synergistic approaches including heterologous vaccination, booster doses, and antiviral drugs have been developed, equitable vaccine uptake remains the foremost strategy to manage pandemic. Although none of the currently approved vaccines are live-attenuated, several reports of disease flares, waning protection, and acute-onset syndromes have emerged as short-term adverse events after vaccination. Hence, scientific literature falls short when discussing potential long-term effects in vulnerable cohorts. The COVAD-2 survey follows on from the baseline COVAD-1 survey with the aim to collect patient-reported data on the long-term safety and tolerability of COVID-19 vaccines in immune modulation. The e-survey has been extensively pilot-tested and validated with translations into multiple languages. Anticipated results will help improve vaccination efforts and reduce the imminent risks of COVID-19 infection, especially in understudied vulnerable groups
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