The relationship between child maltreatment and social anxiety: A meta-analysis

BACKGROUND

Globally, child maltreatment has become a serious problem, affecting individuals' physical and mental health. This meta-analysis aimed to explore the association between child maltreatment and social anxiety.

METHODS

MEDLINE, PsycINFO, EMBASE, Scopus, and Web of Science were searched for articles published by October 2021. Newcastle-Ottawa Quality Assessment Scale (NOS) was used to evaluate the quality of studies. Maltreatment type, sample setting, age, country and measurement instruments were analyzed as moderators.

RESULTS

Twenty-nine studies met the inclusion criteria. Child maltreatment was positively correlated with social anxiety (r = 0.201, 95 % CI [0.171, 0.230]). The moderating effect of maltreatment subtypes was significant whether divided into three types (Q_b = 12.152, p = .002), or five types (Q_b = 11.574, p = .021). Specifically, emotional maltreatment (r = 0.251, 95 % CI [0.201, 0.298]) had a stronger relationship with social anxiety than physical (r = 0.138, 95 % CI [0.085, 0.191]; Q_b = 9.312, p = .002) and sexual maltreatment (r = 0.153, 95 % CI [0.108, 0.197]; Q_b = 9.197, p = .002). However, there was no significant difference in the effect size between physical and sexual maltreatment (Q_b = 0.096, p = .757). Other variables (age, sample setting and country) were not significant moderators.

LIMITATIONS

Only twenty-nine studies were included, and there was high heterogeneity among the studies, the interpretation of the results should be cautious.

CONCLUSIONS

This meta-analysis confirmed the relationship between child maltreatment and social anxiety, especially highlight the harmfulness of emotional maltreatment.

What is the relationship between indoor air quality parameters and airborne microorganisms in hospital environments? A systematic review and meta-analysis

Airborne microorganisms in hospitals have been associated with several hospital-acquired infections (HAIs), and various measures of indoor air quality (IAQ) parameters such as temperature, relative humidity, carbon dioxide (CO₂ ), particle mass concentration, and particle size have been linked to pathogen survival or mitigation of pathogen spread. To investigate whether there are quantitative relationships between the concentration of airborne microorganisms and the IAQ in the hospital environment. Web of Science, Scopus and PubMed databases were searched for studies reporting airborne microbial levels and any IAQ parameter(s) in hospital environments, from database inception to October 2020. Pooled effect estimates were determined via random-effects models. Seventeen of 654 studies were eligible for the meta-analysis. The concentration of airborne microbial measured as aerobic colony count (ACC) was significantly correlated with temperature (r = 0.25 [95% CI = 0.06-0.42], p = 0.01), CO₂ concentration (r = 0.53 [95% CI = 0.40-0.64], p ˂ 0.001), particle mass concentration (≤5 µg/m³ ; r = 0.40 [95% CI = 0.04-0.66], p = 0.03), and particle size (≤5 and ˃5 µm), (r = 0.51 [95% CI = 0.12-0.77], p = 0.01 and r = 0.55 [95% CI = 0.20-0.78], p = 0.003), respectively, while not being significantly correlated with relative humidity or particulate matter of size >5 µm. Conversely, airborne total fungi (TF) were not significantly correlated with temperature, relative humidity, or CO₂ level. However, there was a significant weak correlation between ACC and TF (r = 0.31 [95% CI = 0.07-0.52], p = 0.013). Although significant correlations exist between ACC and IAQ parameters, the relationship is not definitive; the IAQ parameters may affect the microorganisms but are not responsible for the presence of airborne microorganisms. Environmental parameters could be related to the generating source, survival, dispersion, and deposition rate of microorganisms. Future studies should record IAQ parameters and factors such as healthcare worker presence and the activities carried out such as cleaning, sanitizing, and disinfection protocols. Foot traffic would influence both the generation of microorganisms and their deposition rate onto surfaces in the hospital environment. These data would inform models to improve the understanding of the likely concentration of airborne microorganisms and provide an alternative approach for real-time monitoring of the healthcare environment.

Response of Nitrogen Losses to Excessive Nitrogen Fertilizer Application in Intensive Greenhouse Vegetable Production

Excessive nitrogen fertilizer application in greenhouse vegetable production (GVP) is of scientific and public concern because of its significance to international environmental sustainability. We conducted a meta-analysis using 1174 paired observations from 69 publications on the effects of nitrogen fertilizer application and reducing nitrogen fertilizer application on the nitrogen losses on a broad scale. We found that the increase in nitrogen loss is much higher than that in production gain caused by excessive application of nitrogen fertilizer: nitrate leaching (+187.5%), ammonium leaching (+28.1%), total nitrogen leaching (+217.0%), nitrous oxide emission (+202.0%), ammonia emission (+176.4%), nitric oxide emission (+543.3%), yield (+35.7%) and nitrogen uptake (+24.5%). Environmental variables respond nonlinearly to nitrogen fertilizer application, with severe nitrate leaching and nitrous oxide emission when the application rate exceeds 570 kg N/ha and 733 kg/N, respectively. The effect of nitrogen fertilizer on yield growth decreases when the application rate exceeds 302 kg N/ha. Appropriate reduction in nitrogen fertilizer application rate substantially mitigates the environmental cost, for example, decreasing nitrate leaching (−32.4%), ammonium leaching (−6.5%), total nitrogen leaching (−37.3%), ammonia emission (−28.4%), nitrous oxide emission (−38.6%) and nitric oxide emission (−8.0%), while it has no significant effect on the nitrogen uptake and yield.

Multiple-input multiple-output causal strategies for gene selection

BACKGROUND

Traditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting.

RESULTS

We show in synthetic case study that a better prioritization of causal variables can be obtained by considering a relevance score which incorporates a causal term. In addition we show, in a meta-analysis study of six publicly available breast cancer microarray datasets, that the improvement occurs also in terms of accuracy. The biological interpretation of the results confirms the potential of a causal approach to gene selection.

CONCLUSIONS

Integrating causal information into gene selection algorithms is effective both in terms of prediction accuracy and biological interpretation.

A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?

Motivation: Survival prediction of breast cancer (BC) patients independently of treatment, also known as prognostication, is a complex task since clinically similar breast tumors, in addition to be molecularly heterogeneous, may exhibit different clinical outcomes. In recent years, the analysis of gene expression profiles by means of sophisticated data mining tools emerged as a promising technology to bring additional insights into BC biology and to improve the quality of prognostication. The aim of this work is to assess quantitatively the accuracy of prediction obtained with state-of-the-art data analysis techniques for BC microarray data through an independent and thorough framework.

Results: Due to the large number of variables, the reduced amount of samples and the high degree of noise, complex prediction methods are highly exposed to performance degradation despite the use of cross-validation techniques. Our analysis shows that the most complex methods are not significantly better than the simplest one, a univariate model relying on a single proliferation gene. This result suggests that proliferation might be the most relevant biological process for BC prognostication and that the loss of interpretability deriving from the use of overcomplex methods may be not sufficiently counterbalanced by an improvement of the quality of prediction.

Availability: The comparison study is implemented in an R package called survcomp and is available from http://www.ulb.ac.be/di/map/bhaibeka/software/survcomp/.

Contact:bhaibeka@ulb.ac.be

Supplementary information:Supplementary data are available at Bioinformatics online.