Exploring interleukin-6, lipopolysaccharide binding protein and brain-derived neurotrophic factor following 12 weeks of adjunctive minocycline treatment for depression

Objective: This study aimed to explore effects of adjunctive minocycline treatment on inflammatory and neurogenesis markers in major depressive disorder (MDD). Methods: Serum samples were collected from a randomised, placebo-controlled 12-week clinical trial of minocycline (200mg/day, added to treatment as usual) for adults (n=71) experiencing MDD to determine changes in interleukin-6 (IL-6), lipopolysaccharide binding protein (LBP) and brain derived neurotrophic factor (BDNF). General Estimate Equation modelling explored moderation effects of baseline markers and exploratory analyses investigated associations between markers and clinical outcomes. Results: There was no difference between adjunctive minocycline or placebo groups at baseline or week 12 in the levels of IL-6 (week 12; placebo 2.06±1.35 pg/ml; minocycline 1.77±0.79 pg/ml; p=0.317), LBP (week 12; placebo 3.74±0.95 µg/ml; minocycline 3.93±1.33 µg/ml; p=0.525) or BDNF (week 12; placebo 24.28±6.69 ng/ml; minocycline 26.56±5.45 ng/ml; p=0.161). Higher IL-6 levels at baseline were a predictor of greater clinical improvement. Exploratory analyses suggested that the change in IL-6 levels were significantly associated with anxiety symptoms (HAMA; p=0.021) and quality of life (Q-LES-Q-SF; p=0.023) scale scores. No other clinical outcomes were shown to have this mediation effect, nor did the other markers (LBP or BDNF) moderate clinical outcomes. Conclusion: There were no overall changes in IL-6, LBP or BDNF following adjunctive minocycline treatment. Exploratory analyses suggest a potential role of IL-6 on mediating anxiety symptoms with MDD. Future trials may consider enrichment of recruitment by identifying several markers or a panel of factors to better represent an inflammatory phenotype in MDD with larger sample size.

Science Needs Systematic Replicability Audits

The credibility revolution in social science has highlighted the importance of conducting replication studies. Despite this growing awareness, the value of direct replications is still hotly debated. In this article, we identify three main functions served by replication. We argue that replications are valuable when they target important or influential studies, when they provide a general estimate of the replicability rate of a population of published articles, and when they create incentives favoring replicable research. We therefore argue that the scientific community should organize systematic large-scale replication audits of two subsets of journals’ published articles: a subset of the most-cited articles, and a subset of randomly selected articles that would provide an estimate of the replicability of the journals' articles. These replicability audits should pave the way for more general quality audits of scientific journals.

Prevalence and distribution of pain in children

Historically, only a few studies addressed the prevalence of acute and chronic pain in infants, older children, and adolescents across multiple settings. Typically, there was a preponderance of single-site studies that reported local pain prevalence and distribution in children. The generalizability of these results was questionable given small sample sizes; thus, results from local studies garnered only a general estimate at best. However, recent systematic reviews that synthesize evidence and critically appraise the quality of the studies provide a much clearer idea of the prevalence of acute and chronic pain in children. Studies of pain prevalence vary as to the pain and prevalence definitions used, the reporting period (i.e., point prevalence, period prevalence), and stratification by duration of involvement, which makes comparison of findings challenging. In this chapter, we will clarify definitions of prevalence and acute and chronic pain and use these definitions to explore the prevalence and distribution of pain across the broader system that delivers health care to children (e.g., hospitals and in community healthcare settings). Perspectives on clinical practice, knowledge translation, and future research will be shared via a case study and perspective box.

Summary Tables

The three tables that follow provide an easily accessible comparative summary of the key points and features of the major characterization methods discussed in the text. The techniques are classified into three broad groups/methods: spectroscopy/chemical, diffraction/scattering, and imaging. For each technique, a concise description of the method and its use is followed, in bullet form, by its salient details and other characteristics, such as resolution, sensitivity etc., as well as specimen requirements that determine its practice. A general estimate of cost and space requirements is also included, but needless to say, this is only a snapshot for comparison and is definitely subject to change with time.

Improving Estimate at Completion (EAC) Cost of Construction Projects Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Earned Value Management (EVM) is well-known technique for measuring project performance and progress. Due to EVM's attitude to combining cost and time performance simultaneously, project performance can be forecasted accurately and this plays a vital role in the future of the projects. In the current study, the authors employed Adaptive Neuro-Fuzzy Inference System (ANFIS) as a powerful prediction tool to forecast completion cost of the projects considering the percentage of risk for qualitative variables and comparing it with other types of Neural Networks. Since the network structure is usually tuned based on the obtained results, network optimization procedure is applied using a conventional method for estimating cost-caused project breakdown. The results showed ANFIS had a suitable performance (MSE=0.0003) and based on the sensitivity analysis, EV is recognized as the most sensitive factor in the project. This paper improves the general estimate at completion formula by taking uncertain conditions into account.

Potential Effectiveness of Bicycle-Automatic Emergency Braking using the Washtenaw Area Transportation Study Data Set

In the United States, fatalities from vehicle–bicycle crashes have been increasing since 2010. A total of 857 cyclists were struck and killed in 2018 which is an increase from 623 fatalities in 2010. One promising countermeasure is Automatic Emergency Braking (AEB), which can help prevent and/or mitigate many vehicle–bicycle crashes. AEB is a vehicle-based system that can detect and mitigate an impending crash. The goal of this study was to elucidate U.S. vehicle–bicycle crashes and examine related factors to estimate AEB effectiveness. This study used a unique in-depth vehicle–bicycle crash study dataset collected under the collaboration of the Washtenaw Area Transportation Study (WATS) and the Toyota Collaborative Research Center conducted in southeast Michigan from 2011 to 2013. The WATS database provides in-depth investigations of vehicle–bicycle crashes in the United States. The characteristics of the WATS vehicle–bicycle crashes were validated against the Fatality Analysis Reporting System and the General Estimate System. The WATS database cases were examined to estimate the potential effectiveness of AEB to prevent or mitigate vehicle–bicycle collisions. In 60% of the WATS cases, cyclists were in the road for more than 1 s before impact. Assuming that a hypothetical AEB system requires a minimum of 1 s for detection and brake activation, these collisions would potentially be avoided or mitigated. However, for the remaining cases with less than 1 s of time to react (40% of cases), that AEB system would be challenged to avoid or mitigate the collision.

Discovery of 20 Novel Bio-Flocculants Producers and 19 New Bacterial Strains from Three Surface Water In Ilorin, Kwara State, Nigeria

Plummeting the burden that is increasingly posed by water related diseases is amongst the major public health goals for a developing country. The required growth and development experienced in developing countries will always weigh on this important component of earth from either industrial waste, sewage or domestic waste. A general estimate from WHO global evidence synthesis on water related complications and diseases posits that about 94% could be preventable through environmental modification and application of simple techniques to treat water1,2. Amongst other explored purification protocols, flocculation and chlorination as a chemical process has been linked to few health conditions after long time of exposure3. This has been largely adduced to the synthetic source and thus the need to explore flocculants of biological origin that can effectively purify water and presents little or no health risk. Surface water, which is often categorized asharborof large numbers of microorganisms amongst other characteristics was, explored for the presence of bacteria flocculants producers from three surface water in Ilorin, Kwara State in Nigeria.

Methodological approaches to the assessment of state control and inspection system by the citizens as its beneficiaries

The success of the reform of the state control and inspection system depends significantly on taking into account the estimates of all stakeholders, i.e. business, government, and citizens as beneficiaries of the state control and inspection activities; and such estimates should be considered at the stages of planning, monitoring, and evaluating the achieved results. The priority project ‘Reforming State Control and Inspection System in the Russian Federation’ considers citizens’ estimates only if presented by statistical indicators that reflect the nature, frequency, and scale of harm often measured by the control bodies themselves, i.e. such data are prone to distortion. The authors suggest to supplement this system by indicators allowing the citizens to assess the state control and inspection activities and reform. These indicators can be measured in representative surveys, which would allow to balance the existing system of monitoring the state control and inspection activities. The article presents both international and Russian approaches to the sociological study of the efficiency of the state control and inspection system. The authors identify methodological rules of such surveys, such as that all adult citizens should be questioned regardless of their experience of interaction with control bodies. The article also describes the types of public legally protected values and the types of risks that should be part of sociological questionnaires; and suggests some key indicators for the assessment of the state control activities by the citizens: the frequency of facing the need to protect public values (the lower the frequency, the higher the effectiveness); the general estimate of the public values safety (by groups of risks); and the results of citizens’ interaction with the control bodies to protect public values including damage compensation.

DEPLOYMENT OF THE HIGHER EDUCATION POLICY FOR THE TRAINING OF LIBRAS TEACHERS

The aim of this article is to identify and analyze the deployment of the higher education policies directed at the training of Brazilian Sign Language teachers in the upcoming decade, under decree 5.626 from 2005. Documentary research, based on content analysis, allowed the outlining of a panorama of the courses offered at federal higher education institutions in Bilingual Pedagogy and a Degree in Libras, from 2006 to 2015. The results revealed that the distance learning modality was the main one linked to the expansion of the number of vacancies. However, in a general estimate, there is still a small number of vacancies and higher education courses for these professionals, especially in the segment that includes early childhood education and the first years of elementary school. This shows the lack of graduates from these courses, in the national scenario.

NEURAL NETWORK ALGORITHM FOR CHOOSING METHODS OF TIME SERIES FORECASTING

The prediction unit is one of the most important components of intelligent control systems. The results of its operation influence the type of control actions generated by the system. The performance of the unit depends on the prediction methods used. The accuracy of the result of the prediction block depends on the choice of the forecasting method. Thus, uncertainty when choosing a forecasting method is a factor that has a negative impact on the reliability of the result of the prediction block and, as consequence, on the reliability of the control system as a whole. The analysis results of the work in this field suggest that the problem of choosing the best forecasting methods has been worked out mainly at the conceptual level. The disadvantages of the considered works are the lack of specifying the mechanism for implementing the proposed algorithms, as well as the potential result of their work is a wide group of prediction methods that are found optimal. In one of the considered works, the expert system is indicated as a mechanism for solving the problem, and the algorithm for modifying and updating the rules is not specified. We have proposed the algorithm based on the use of a precedent analysis method realized in artificial neural networks, which allows to solve these problems. The statistical indicators of the time series, as well as the forecast horizon are used as characteristics of the object and the forecasting task, which constitute the set of attributes of a precedent. A set of solutions to the problem are the applied prediction methods. The set of results is a general estimate of the solution calculated on the basis of the values of the optimality criteria. At the same time, estimation of the optimality of the forecasting method is performed on the basis of the criteria of accuracy and speed, which are based on the prediction error, as well as the length of time spent on obtaining the forecast. The effectiveness of the proposed algorithm has been proved by the results of the experiment.