Leveraging genetics to investigate causal effects of immune cell phenotypes in periodontitis: a mendelian randomization study.

Introduction: Immune cells are dynamic in the inflammatory environment and play a key role in eradicating periodontal pathogens, modulating immune responses, and instigating tissue destruction. Identifying specific immune cell phenotypes associated with periodontitis risk is essential for targeted immunotherapeutic interventions. However, the role of certain specific immune cell phenotypes in the development of periodontitis is unknown. Mendelian randomization offers a novel approach to reveal causality and address potential confounding factors through genetic instruments. Methods: This two-sample Mendelian randomization study assessed the causal relationship between 731 immune cell phenotypes and periodontitis using the inverse variance weighting method with the GWAS catalog genetic database. Methodological robustness was ensured through Cochran's Q test, MR-Egger regression, MR-PRESSO, and Leave-One-Out analysis. Results: 14 immune cell phenotypes showed potential positive causal associations with periodontitis risk (p < 0.05), suggesting an increased risk, while 11 immune cell phenotypes exhibited potential negative causal associations (p < 0.05), indicating a reduced risk. No significant heterogeneity or pleiotropy was observed. Conclusion: This study underscores certain immune cell types as potential periodontitis risk biomarkers, laying a theoretical foundation for future individualized treatment and precision medicine development.

Comparative Analysis of Micrometer-Sized Particle Deposition in the Olfactory Regions of Adult and Pediatric Nasal Cavities: A Computational Study.

Direct nose-to-brain drug delivery, a promising approach for treating neurological disorders, faces challenges due to anatomical variations between adults and children. This study aims to investigate the spatial particle deposition of micron-sized particles in the nasal cavity among adult and pediatric subjects. This study focuses on the olfactory region considering the effect of intrasubject parameters and particle properties. Two child and two adult nose models were developed based on computed tomography (CT) images, in which the olfactory region of the four nasal cavity models comprises 7% to 10% of the total nasal cavity area. Computational Fluid Dynamics (CFD) coupled with a discrete phase model (DPM) was implemented to simulate the particle transport and deposition. To study the deposition of micrometer-sized drugs in the human nasal cavity during a seated posture, particles with diameters ranging from 1 to 100 µm were considered under a flow rate of 15 LPM. The nasal cavity area of adults is approximately 1.2 to 2 times larger than that of children. The results show that the regional deposition fraction of the olfactory region in all subjects was meager for 1-100 µm particles, with the highest deposition fraction of 5.7%. The deposition fraction of the whole nasal cavity increased with the increasing particle size. Crucially, we identified a correlation between regional deposition distribution and nasal cavity geometry, offering valuable insights for optimizing intranasal drug delivery.

Analysis of prevention through design studies in construction: A subject review.

INTRODUCTION: The concept of addressing and minimizing construction site safety risks in the early phase of a project has generated research interest, especially since the National Institute for Occupational Safety and Health (NIOSH) launched its national Prevention through Design (PtD) initiative in July 2007. In the last decade, several studies on PtD with differeing goals and methods have been published in construction journals. To date, few systematic examinations of the development and trends associated with PtD research have been conducted in the discipline. METHOD: This paper presents a study of the latest PtD research trends in construction safety management through analysis of publications in prominent construction journals from 2008 to 2020. Both descriptive and content analyses were conducted based on the number of papers published annually and clusters of topics covered in the papers. RESULTS: The study shows an increasing interest in PtD research in recent years. Research topics covered mainly focus on the perspectives of PtD stakeholders, PtD resources/tools/procedures, and technology applications to facilitate PtD implementation in practice. This review study provides an improved understanding of the state-of-the-art of PtD research in terms of accomplishments and research gaps. The study also compares the findings from journal articles with industry best practices related to PtD to guide future research in this domain. PRACTICAL APPLICATION: This review study is of significant value to researchers to overcome the limitations of the current PtD studies, and to extend the scope of PtD research, and can be used by industry professionals when considering and selecting appropriate PtD resources/tools in practice.

Diagnosis of each main coronary artery stenosis based on whale optimization algorithm and stacking model.

Cardiovascular disease is currently one of the diseases with high morbidity and mortality worldwide. One of the main types is coronary artery disease (CAD), which occurs when one or more of the three main arteries, the left anterior descending (LAD) artery, the left circumflex (LCX) artery, and the right coronary artery (RCA), are narrowed. In this paper, we introduce a computer-aided diagnosis model, which uses the k-nearest neighbor (KNN)-based whale optimization algorithm (WOA) for feature selection and combines stacking model for CAD diagnosis and prediction. In WOA, the values in the solution vectors are all continuous, and a threshold is set for binary-conversion to obtain the optimal feature subsets of each main coronary artery. Then we develop a two-layer stacking model based on the selected feature subsets to diagnosis LAD, LCX and RCA. By the proposed method, we select 17 features for each main artery diagnosis, and the classification accuracy on LAD, LCX, and RCA test sets is 89.68, 88.71 and 85.81%, respectively. On the Z-Alizadeh Sani dataset, we compare the proposed feature selection method with other metaheuristics and compare the performance of WOA based on different wrappers. The experimental results show that, the KNN-based WOA method selects the optimal feature subsets, and the classification performance of the stacking model is better than other machine learning algorithms.

Safety and health management response to COVID-19 in the construction industry: A perspective of fieldworkers.

The COVID-19 outbreak has significantly impacted the construction industry. The pandemic can exacerbate an already dire safety and health situation in the industry and negatively impact construction employees and employers. The present study investigates the safety and health measures implemented by construction firms in the United States (US), their effectiveness and usefulness, and workers' satisfaction with these COVID-19 measures. A questionnaire survey was developed and distributed to construction fieldworkers in the US to collect their perspectives on the implemented COVID-19 measures in the construction industry. A total of 187 valid responses were received and analyzed to achieve the aim of the study. Results revealed that strategies implemented to increase social distance and minimize group gathering to 10 persons in certain workstations were perceived to be substantially more effective than job-site screening strategies. Furthermore, smaller contractors implemented fewer safety measures and perceived them to be significantly less effective than those used by medium- and large-sized contractors. Fieldworkers were favorably disposed toward using technologies, such as video-conferencing apps and wearable sensing devices, to slow the spread of COVID-19 on construction job sites. The present study contributes to the body of knowledge by identifying safety and health measures to mitigate the spread of COVID-19 in construction. Practically, the study findings provide valuable insights to inform the successful implementation of safety strategies in the construction industry during a pandemic. The results are crucial for industry practitioners responsible for developing and revising pre- and post-pandemic safety and health plans.

Dopamine-modified poly(styrene) nanospheres as new high-speed adsorbents for copper-ions having enhanced smoke-toxicity-suppression and flame-retardancy.

Polydopamine-coated polystyrene (PS@PDA) nanospheres which are prepared by self-polymerizing of dopamine on the surfaces of polystyrene (PS) nanospheres show excellent Cu2+ adsorption capacity. The Cu2+ adsorption capacity of PS@PDA can even reach 178 mg/g in about 6 min, which is superior to the other adsorption materials reported in literatures. Through linear fitting, it can be seen that Cu2+ is chemisorption covered by multilayers on the surface of PS@PDA, with less affect by temperature. The PS@PDA nanosphere with good adsorption capacity is first applied as the Cu2+ adsorbent and then recycled to preparation of PS nanocomposite with enhanced flame retardancy, great smoke and toxic gases suppression properties. To overcome the drawbacks of evaluation methods reported before, a new evaluation system of analytic hierarchy process is first applied to comprehensively analyze fire safety of samples. The average value of smoke production rate of PS@PDA absorbed 5 mg/L Cu2+ (PS 2) reduces by about 10%, and the average and total yield of carbon monoxide of PS 2 decrease by 15.7% and 18.1% compared with that of neat PS, respectively. PS 2 with the highest score of 86.75 has the best comprehensive fire safety performance among all samples. This work provides a guideline for green flame-retardant chemistry.