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The effect of a second dose of vinasse on some physical and chemical properties of a Vertisol was evaluated with cut sugarcane in Valle del Cauca, Colombia. We analyzed the expression of any possible toxicity in foliar tissue samples at the end of the cycle. Vinasse treatments added with two different mixtures of microorganisms were established in a randomized complete block with five replicates: soil without vinasse and no microorganisms; soils with a dose of vinasse, soils with two doses of vinasse; soils with a single dose of vinasse separately evaluated with two different mixtures of microorganisms and soils with two doses of vinasse separately evaluated with two different mixtures of microorganisms. The chemical properties evaluated were pH, soil organic matter content, C, N, P, Ca, Mg, Na, and K content, cation exchange capacity CEC and electrical conductivity EC. The physical properties determined were bulk density, particle density and porosity. There were no significant differences in the physical and chemical properties of the soil for the evaluated cultivation cycle with the application of one or two doses of vinasse with and without microorganisms. Leaf tissue analysis did not show a nutritional imbalance due to the second application.

Abstract Background This paper proposes a novel framework for bioinformatics assisted biosurveillance and early warning to address the inefficiencies in traditional surveillance as well as the need for more timely and comprehensive infection monitoring and control. It leverages on breakthroughs in rapid, high-throughput molecular profiling of microorganisms and text mining. Results This framework combines the genetic and geographic data of a pathogen to reconstruct its history and to identify the migration routes through which the strains spread regionally and internationally. A pilot study of Salmonella typhimurium genotype clustering and temporospatial outbreak analysis demonstrated better discrimination power than traditional phage typing. Half of the outbreaks were detected in the first half of their duration. Conclusion The microbial profiling and biosurveillance focused text mining tools can enable integrated infectious disease outbreak detection and response environments based upon bioinformatics knowledge models and measured by outcomes including the accuracy and timeliness of outbreak detection.

Abstract Background The adoption of new medicines is influenced by a complex set of social processes that have been widely examined in terms of individual prescribers’ information-seeking and decision-making behaviour. However, quantitative, population-wide analyses of how long it takes for new healthcare practices to become part of mainstream practice are rare. Methods We applied a Bass diffusion model to monthly prescription volumes of 103 often-prescribed drugs in Australia (monthly time series data totalling 803 million prescriptions between 1992 and 2010), to determine the distribution of adoption rates. Our aim was to test the utility of applying the Bass diffusion model to national-scale prescribing volumes. Results The Bass diffusion model was fitted to the adoption of a broad cross-section of drugs using national monthly prescription volumes from Australia (median R2 = 0.97, interquartile range 0.95 to 0.99). The median time to adoption was 8.2 years (IQR 4.9 to 12.1). The model distinguished two classes of prescribing patterns – those where adoption appeared to be driven mostly by external forces (19 drugs) and those driven mostly by social contagion (84 drugs). Those driven more prominently by internal forces were found to have shorter adoption times (p = 0.02 in a non-parametric analysis of variance by ranks). Conclusion The Bass diffusion model may be used to retrospectively represent the patterns of adoption exhibited in prescription volumes in Australia, and distinguishes between adoption driven primarily by external forces such as regulation, or internal forces such as social contagion. The eight-year delay between the introduction of a new medicine and the adoption of the prescribing practice suggests the presence of system inertia in Australian prescribing practices.

Abstract Background Online social networking and personally controlled health management systems (PCHMS) offer a new opportunity for developing innovative interventions to prevent diseases of public health concern (e.g., influenza) but there are few comparative studies about patterns of use and impact of these systems. Methods/Design A 2010 CONSORT-compliant randomised controlled trial with a two-group parallel design will assess the efficacy of a web-based PCHMS called Healthy.me in facilitating the uptake of influenza vaccine amongst university students and staff. Eligible participants are randomised either to obtain access to Healthy.me or a 6-month waitlist. Participants complete pre-study, post-study and monthly surveys about their health and utilisation of health services. A post-study clinical audit will be conducted to validate self-reports about influenza vaccination and visits to the university health service due to influenza-like illness (ILI) amongst a subset of participants. 600 participants older than 18 years with monthly access to the Internet and email will be recruited. Participants who (i) discontinue the online registration process; (ii) report obtaining an influenza vaccination in 2010 before the commencement of the study; or (iii) report being influenced by other participants to undertake influenza vaccination will be excluded from analysis. The primary outcome measure is the number of participants obtaining influenza vaccination during the study. Secondary outcome measures include: number of participants (i) experiencing ILI symptoms, (ii) absent from or experiencing impairment in work or study due to ILI symptoms, (iii) using health services or medications due to ILI symptoms; (iv) expressing positive or negative attitudes or experiences towards influenza vaccination, via their reasons of receiving (or not receiving) influenza vaccine; and (v) their patterns of usage of Healthy.me (e.g., frequency and timing of hits, duration of access, uptake of specific functions). Discussion This study will provide new insights about the utility of online social networking and PCHMS for public health and health promotion. It will help to assess whether a web-based PCHMS, with connectivity to a health service provider, containing information and self-management tools, can improve the uptake of preventive health services amongst university students and staff. Trial registration ACTRN12610000386033 (Australian New Zealand Clinical Trials Registry)

Background: There is an emerging understanding that coronavirus disease 2019 (COVID-19) is associated with increased incidence of pneumomediastinum. We aimed to determine its incidence among patients hospitalised with COVID-19 in the United Kingdom and describe factors associated with outcome., Methods: A structured survey of pneumomediastinum and its incidence was conducted from September 2020 to February 2021. United Kingdom-wide participation was solicited via respiratory research networks. Identified patients had SARS-CoV-2 infection and radiologically proven pneumomediastinum. The primary outcomes were to determine incidence of pneumomediastinum in COVID-19 and to investigate risk factors associated with patient mortality., Results: 377 cases of pneumomediastinum in COVID-19 were identified from 58 484 inpatients with COVID-19 at 53 hospitals during the study period, giving an incidence of 0.64%. Overall 120-day mortality in COVID-19 pneumomediastinum was 195/377 (51.7%). Pneumomediastinum in COVID-19 was associated with high rates of mechanical ventilation. 172/377 patients (45.6%) were mechanically ventilated at the point of diagnosis. Mechanical ventilation was the most important predictor of mortality in COVID-19 pneumomediastinum at the time of diagnosis and thereafter (p<0.001) along with increasing age (p<0.01) and diabetes mellitus (p=0.08). Switching patients from continuous positive airways pressure support to oxygen or high flow nasal oxygen after the diagnosis of pneumomediastinum was not associated with difference in mortality., Conclusions: Pneumomediastinum appears to be a marker of severe COVID-19 pneumonitis. The majority of patients in whom pneumomediastinum was identified had not been mechanically ventilated at the point of diagnosis., Competing Interests: Conflict of interest: James Melhorn has nothing to disclose. Conflict of interest: Andrew Achaiah has nothing to disclose. Conflict of interest: Francesca M. Conway has nothing to disclose. Conflict of interest: Elizabeth M. F. Thompson has nothing to disclose. Conflict of interest: Erik W. Skyllberg has nothing to disclose. Conflict of interest: Joseph Durrant has nothing to disclose. Conflict of interest: Neda A. Hasan has nothing to disclose. Conflict of interest: Yasser Madani has nothing to disclose. Conflict of interest: Prasheena Naran has nothing to disclose. Conflict of interest: Bavithra Vijayakumar has nothing to disclose. Conflict of interest: Matthew J. Tate has nothing to disclose. Conflict of interest: Gareth E. Trevelyan has nothing to disclose. Conflict of interest: Irfan Zaki has nothing to disclose. Conflict of interest: Catherine A. Doig has nothing to disclose. Conflict of interest: Geraldine Lynch has nothing to disclose. Conflict of interest: Gill Warwick has nothing to disclose. Conflict of interest: Avinash Aujayeb has nothing to disclose. Conflict of interest: Karl A. Jackson has nothing to disclose. Conflict of interest: Hina Iftikhar has nothing to disclose. Conflict of interest: Jonathan H. Noble has nothing to disclose. Conflict of interest: Anthony Y. K. C. Ng has nothing to disclose. Conflict of interest: Mark Nugent has nothing to disclose. Conflict of interest: Philip J. Evans has nothing to disclose. Conflict of interest: A. Hastings has nothing to disclose. Conflict of interest: Harry R. Bellenberg has nothing to disclose. Conflict of interest: Hannah Lawrence has nothing to disclose. Conflict of interest: Rachel L. Saville has nothing to disclose. Conflict of interest: Nikolas T. Johl has nothing to disclose. Conflict of interest: Adam N. Grey has nothing to disclose. Conflict of interest: Huw C. Ellis has nothing to disclose. Conflict of interest: Cheng Chen has nothing to disclose. Conflict of interest: Thomas L. Jones has nothing to disclose. Conflict of interest: Nadeem Maddekar has nothing to disclose. Conflict of interest: Shahul Leyakathali Khan has nothing to disclose. Conflict of interest: Ambreen Iqbal Muhammad has nothing to disclose. Conflict of interest: Hakim Ghani has nothing to disclose. Conflict of interest: Yadee Maung Maung Myint has nothing to disclose. Conflict of interest: Cecillia Rafique has nothing to disclose. Conflict of interest: Benjamin J. Pippard has nothing to disclose. Conflict of interest: Benjamin R. H. Irving has nothing to disclose. Conflict of interest: Fawad Ali has nothing to disclose. Conflict of interest: Viola H. Asimba has nothing to disclose. Conflict of interest: Aqeem Azam has nothing to disclose. Conflict of interest: Eleanor C. Barton has nothing to disclose. Conflict of interest: Malvika Bhatnagar has nothing to disclose. Conflict of interest: Matthew P. Blackburn has nothing to disclose. Conflict of interest: Kate J. Millington has nothing to disclose. Conflict of interest: Nicholas J. Budhram has nothing to disclose. Conflict of interest: Katherine L. Bunclark has nothing to disclose. Conflict of interest: Toshit P. Sapkal has nothing to disclose. Conflict of interest: Giles Dixon has nothing to disclose. Conflict of interest: Andrew J. E. Harries has nothing to disclose. Conflict of interest: Mohammad Ijaz has nothing to disclose. Conflict of interest: Vijayalakshmi Karunanithi has nothing to disclose. Conflict of interest: Samir Naik has nothing to disclose. Conflict of interest: Malik Aamaz Khan has nothing to disclose. Conflict of interest: Karishma Savlani has nothing to disclose. Conflict of interest: Vimal Kumar has nothing to disclose. Conflict of interest: Beatriz Lara Gallego has nothing to disclose. Conflict of interest: Noor A. Mahdi has nothing to disclose. Conflict of interest: Caitlin Morgan has nothing to disclose. Conflict of interest: Neena Patel has nothing to disclose. Conflict of interest: Elen W. Rowlands has nothing to disclose. Conflict of interest: Matthew S. Steward has nothing to disclose. Conflict of interest: Richard S. Thorley has nothing to disclose. Conflict of interest: Rebecca L. Wollerton has nothing to disclose. Conflict of interest: Sana Ullah has nothing to disclose. Conflict of interest: David M. Smith has nothing to disclose. Conflict of interest: Wojciech Lason has nothing to disclose. Conflict of interest: Anthony J Rostron has nothing to disclose. Conflict of interest: Najib M Rahman has nothing to disclose. Conflict of interest: Rob J Hallifax has nothing to disclose., (Copyright ©The authors 2022.)