Little is known regarding the time trend of mass shootings and associated risk factors. In the current study, we intended to explore the time trend and relevant risk factors for mass shootings in the ...U.S. We attempted to identify factors associated with incidence rates of mass shootings at the population level. We evaluated if state-level gun ownership rate, serious mental illness rate, poverty percentage, and gun law permissiveness could predict the state-level mass shooting rate, using the Bayesian zero-inflated Poisson regression model. We also tested if the nationwide incidence rate of mass shootings increased over the past three decades using the non-homogenous Poisson regression model. We further examined if the frequency of online media coverage and online search interest levels correlated with the interval between two consecutive incidents. The results suggest an increasing trend of mass shooting incidences over time (p < 0.001). However, none of the state-level variables could predict the mass shooting rate. Interestingly, we have found inverse correlations between the interval between consecutive shootings and the frequency of on-line related reports as well as on-line search interests, respectively (p < 0.001). Therefore, our findings suggest that online media might correlate with the increasing incidence rate of mass shootings. Future research is warranted to continue monitoring if the incidence rates of mass shootings change with any population-level factors in order to inform us of possible prevention strategies.
DNA methylation (DNAm) profiles in central airway epithelial cells (AECs) may play a key role in pathological processes in asthma. The goal of the current study is to compare the diagnostic ...performance of DNAm markers across three tissues: AECs, nasal epithelial cells (NECs), and peripheral blood mononuclear cells (PBMCs). Additionally, we focused on the results using the machine learning algorithm in the context of multi-locus effects to evaluate the diagnostic performance of the optimal subset of CpG sites. We obtained 74 subjects with asthma and 41 controls from AECs, 15 subjects with asthma and 14 controls from NECs, 697 subjects with asthma and 97 controls from PBMCs. Epigenome-wide DNA methylation levels in AECs, NECs and PBMCs were measured using the Infinium Human Methylation 450 K BeadChip. Overlap analysis across the three different sample sources at the locus and pathway levels were studied to investigate shared or unique pathophysiological processes of asthma across tissues. Using the top 100 asthma-associated methylation markers as classifiers from each dataset, we found that both AEC- and NEC-based DNAm signatures exerted a lower classification error than the PBMC-based DNAm markers (p-value = 0.0002). The area-under-the-curve (AUC) analysis based on out-of-bag errors using the random forest classification algorithm revealed that PBMC-, NEC-, and AEC-based methylation data yielded 31 loci (AUC: 0.87), 8 loci (AUC: 0.99), and 4 loci (AUC: 0.97) from each optimal subset of tissue-specific markers, respectively. We also discovered the locus-locus interaction of DNAm levels of the CDH6 gene and RAPGEF3 gene might interact with each other to jointly predict the risk of asthma - which suggests the pivotal role of cell-cell junction in the pathological changes of asthma. Both AECs and NECs might provide better diagnostic accuracy and efficacy levels than PBMCs. Further research is warranted to evaluate how these tissue-specific DNAm markers classify and predict asthma risk.
Abstract
Background
It is unclear to familial screen time in early childhood is associated with the subsequent diagnosis of attention-deficit and hyperactivity disorder (ADHD). Our study is to ...evaluate the association between screen time during early childhood in families and the incidence of ADHD.
Methods
We conducted a population-based birth cohort study by using the Taiwan Birth Cohort Study, which recruited 24 200 mother–child pairs when children were 6 months old. Screen time exposure for children and parents were collected at the age of 18 and 36 months. Whether the child has ever been diagnosed with ADHD was determined at a follow-up interview at age 8. Factors including socioeconomic factors and screen time were analyzed using logistic regression to determine their association with the rate of ADHD.
Results
A total of 16 651 term singletons were included in the final analysis. Of them, 382 (2.3%) were diagnosed as having ADHD before the age of 8 years. No significant relationship between children’s or fathers’ screen time and ADHD was noted. When compared to children whose mothers spent less time on screens, those whose mothers spent more than 3 h a day on screens when the child was 3 years old exhibited a higher incidence of ADHD (adjusted OR aOR: 1.31, 95% CI: 1.03–1.66).
Conclusion
Higher maternal screen time when the child was 3 years old was associated with an increased incidence of ADHD in this population-based study. However, children’s screen time did not find related to ADHD. We found that it was the mother’s screen time, who typically serves as the primary caregiver in our study participants, not the child’s, that mattered. In addition to superficial screen use time, future research is needed to replicate the findings and clarify mechanisms underlying this association.
In this study, we fabricated gelatin/nano-hydroxyapatite/metformin scaffold (GHMS) and compared its effectiveness in bone regeneration with extraction-only, Sinbone, and Bio-Oss Collagen
groups in a ...critical size rat alveolar bone defect model. GHMS was synthesized by co-precipitating calcium hydroxide and orthophosphoric acid within gelatin solution, incorporating metformin, and cross-linked by microbial transglutaminase. The morphology, characterization, and biocompatibility of scaffold were examined. The in vitro effects of GHMS on osteogenic gene and protein expressions were evaluated. In vivo bone formation was assessed in a critical size rat alveolar bone defect model with micro-computed tomography and histological examination by comparing GHMS with extraction-only, Sinbone, and Bio-Oss Collagen
. The synthesized GHMS had a highly interconnected porous structure with a mean pore size of 81.85 ± 13.8 µm. GHMS exhibited good biocompatibility; promoted ALPL, RUNX2, SP7, BGLAP, SPARC and Col1a1 gene expressions; and upregulated the synthesis of osteogenic proteins, including osteonectin, osteocalcin, and collagen type I. In critical size rat alveolar bone defects, GHMS showed superior bone regeneration compared to extraction-only, Sinbone, and Bio-Oss Collagen
groups as manifested by greater alveolar ridge preservation, while more bone formation with a lower percentage of connective tissue and residual scaffold at the defect sites grafted with GHMS in histological staining. The GHMS presented in this study may be used as a potential bone substitute to regenerate alveolar bone. The good biocompatibility, relatively fast degradation, interconnected pores allowing vascularization, and higher bioactivity properties of the components of the GHMS (gelatin, nHA, and metformin) may contribute to direct osteogenesis.
•Australia's National Mental Health Plans (NMHPs) aim to improve mental health and reduce suicide rates.•The plans have evolved from clinical interventions to community-driven approaches.•The ...influence of the NMHPs varies across the nation, which highlights the need to explore how the plans are translated into concrete actions and interventions within the local jurisdictions.•The decrease in suicides during the pandemic emphasizes the need for adaptable prevention strategies.•The insights of this study are valuable for shaping future mental health policies and health services research.
Nearly 3,000 Australians tragically end their lives by suicide each year, underscoring a major national public health challenge with substantial socio-economic ramifications. Australia's National Mental Health Plans (NMHPs) aim to improve mental health and reduce suicide rates. This study investigates their effectiveness by analyzing how age-standardized suicide rates across Australian jurisdictions have fluctuated alongside the implementation of five NMHPs from 1987 to 2021. Findings reveal mixed impacts, with some plans linked to decreases and others associated with increases in suicide rates across different periods and regions. Notably, the recent decline in 2020 requires careful consideration amidst COVID-19 pandemic influences. These insights not only provide valuable evidence for shaping future mental health policies and initiatives but also for future health services research.
•Machine learning selects suicide/self-harm predictors from vast variables.•Machine learning can improve prediction accuracy for suicide/self-harm risk.•Predictors such as parental support indicate ...novel points of prevention and intervention.
This study aimed to use machine learning (ML) models to predict the risk of self-harm and suicide attempts in adolescents. We conducted secondary analysis of cross-sectional data from the Longitudinal Study of Australian Children dataset. Several key variables at the age of 14–15 years were used to predict self-harm or suicide attempt at 16-17 years. Random forest classification models were used to select the optimal subset of predictors and subsequently make predictions. Among 2809 participants, 296 (10.54%) reported an act of self-harm and 145 (5.16%) reported attempting suicide at least once in the past 12 months. The area under the receiver operating curve was fair for self-harm (0.7397) and suicide attempt (0.7220), which outperformed the prediction strategy solely based on prior suicide or self-harm attempt (AUC: 0.6). The most important factors identified were similar, and included depressed feelings, strengths and difficulties questionnaire scores, perceptions of self, and school- and parent-related factors. The random forest classification algorithm, an ML technique, can effectively select the optimal subset of predictors from hundreds of variables to forecast the risks of suicide and self-harm among adolescents. Further research is needed to validate the utility and scalability of ML techniques in mental health research.
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