SNP-based and haplotype-based genome-wide association on drug dependence in Han Chinese.

BACKGROUND: Drug addiction is a serious problem worldwide and is influenced by genetic factors. The present study aimed to investigate the association between genetics and drug addiction among Han Chinese. METHODS: A total of 1000 Chinese users of illicit drugs and 9693 healthy controls were enrolled and underwent single nucleotide polymorphism (SNP)-based and haplotype-based association analyses via whole-genome genotyping. RESULTS: Both single-SNP and haplotype tests revealed associations between illicit drug use and several immune-related genes in the major histocompatibility complex (MHC) region (SNP association: log10BF = 15.135, p = 1.054e-18; haplotype association: log10BF = 20.925, p = 2.065e-24). These genes may affect the risk of drug addiction via modulation of the neuroimmune system. The single-SNP test exclusively reported genome-wide significant associations between rs3782886 (SNP association: log10BF = 8.726, p = 4.842e-11) in BRAP and rs671 (SNP association: log10BF = 7.406, p = 9.333e-10) in ALDH2 and drug addiction. The haplotype test exclusively reported a genome-wide significant association (haplotype association: log10BF = 7.607, p = 3.342e-11) between a region with allelic heterogeneity on chromosome 22 and drug addiction, which may be involved in the pathway of vitamin B12 transport and metabolism, indicating a causal link between lower vitamin B12 levels and methamphetamine addiction. CONCLUSIONS: These findings provide new insights into risk-modeling and the prevention and treatment of methamphetamine and heroin dependence, which may further contribute to potential novel therapeutic approaches.

Deep-learning architecture for PM2.5 concentration prediction: A review.

Accurately predicting the concentration of fine particulate matter (PM2.5) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM2.5 concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM2.5 prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM2.5 by comparing their complexity and effectiveness. We categorized PM2.5 DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM2.5 predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM2.5 prediction and improve the quality of related studies.

The investigation of the role of oral-originated Prevotella-induced inflammation in childhood asthma.

Background and objectives: The oral and gut microbiota play significant roles in childhood asthma pathogenesis. However, the communication dynamics and pathogenic mechanisms by which oral microbiota influence gut microbiota and disease development remain incompletely understood. This study investigated potential mechanisms by which oral-originated gut microbiota, specifically Prevotella genus, may contribute to childhood asthma etiology. Methods: Oral swab and fecal samples from 30 asthmatic children and 30 healthy controls were collected. Microbiome composition was characterized using 16S rRNA gene sequencing and metagenomics. Genetic distances identified potential oral-originated bacteria in asthmatic children. Functional validation assessed pro-inflammatory properties of in silico predicted microbial mimicry peptides from enriched asthma-associated species. Fecal metabolome profiling combined with metagenomic correlations explored links between gut microbiota and metabolism. HBE cells treated with Prevotella bivia culture supernatant were analyzed for lipid pathway impacts using UPLC-MS/MS. Results: Children with asthma exhibited distinct oral and gut microbiota structures. Prevotella bivia, P. disiens, P. oris and Bacteroides fragilis were enriched orally and intestinally in asthmatics, while Streptococcus thermophilus decreased. P. bivia, P. disiens and P. oris in asthmatic gut likely originated orally. Microbial peptides induced inflammatory cytokines from immune cells. Aberrant lipid pathways characterized asthmatic children. P. bivia increased pro-inflammatory and decreased anti-inflammatory lipid metabolites in HBE cells. Conclusion: This study provides evidence of Prevotella transfer from oral to gut microbiota in childhood asthma. Prevotella's microbial mimicry peptides and effects on lipid metabolism contribute to disease pathogenesis by eliciting immune responses. Findings offer mechanistic insights into oral-gut connections in childhood asthma etiology.

[Research Progress on Characteristics of Human Microplastic Pollution and Health Risks].

Microplastic pollution is not only an environmental problem but also a social problem. Many studies have been conducted on the sources, abundance, and distribution of microplastics in the environment, but an understanding of human exposure levels and potential health risks remains very limited. Based on the bibliometric methods, the present review systematically summarized the exposure pathways of microplastics in humans, and then the characteristics and potential adverse impacts on human health were expounded upon. Available literature showed that microplastics in human bodies were mainly concentrated on sizes smaller than 50 µm, and polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET) were the main polymers. Microplastics in environments entered human bodies mainly through food and respiratory pathways, then accumulated in lung and gastrointestinal tissues. Most importantly, small-sized microplastics could distribute in tissues and organs via the circulatory system. The results from lab-based toxicological experiments showed that microplastics not only posed threats to cell membrane integrity, immune stress, gut microbiota, and energy metabolism but also had potentially adverse impacts on the reproductive system. To further understand the health risks of microplastic pollution, it is necessary to promote research on the toxicological effects of microplastics as well as the inner mechanisms and also to establish risk assessment frameworks for evaluating microplastic pollution. These works are crucial to preventing the risks of microplastic pollution with scientific evidence.

Tissue accumulation of microplastics and potential health risks in human.

Microplastics have become ubiquitous throughout the environment. Humans constantly ingest and inhale microplastics, increasing concerns about the health risks of microplastic exposure. However, limited data impedes a full understanding of the internal exposure to microplastics. Herein, to evaluate microplastic exposure via the respiratory and digestive systems, we used laser direct infrared spectroscopy to identify microplastics >20 µm in size in different human tissues. Consequently, 20-100 µm microplastics were concentrated in all tissues, with polyvinyl chloride (PVC) being the dominant polymer. The highest abundance of microplastics was detected in lung tissue with an average of 14.19 ± 14.57 particles/g, followed by that in the small intestine, large intestine, and tonsil (9.45 ± 13.13, 7.91 ± 7.00, and 6.03 ± 7.37 particles/g, respectively). The abundance of microplastics was also significantly greater in females than in males (p < 0.05). Despite significant diversity, our estimation showed that the lungs accumulated the highest amounts of microplastic. Moreover, PVC particles may cause potential health risks because of their high polymer hazard index and maximal risk level. This study provides evidence regarding the occurrence of microplastics in humans and empirical data to support assessments of the health risks posed by microplastics.

Cancer Stem Cell Biomarkers in the Nervous System.

Cancer stem cells (CSCs) have been increasingly recognized in recent years. CSCs from human neural tumors are one of the root causes of metastatic tumor progression, therapeutic resistance and recurrence. However, there is a lack of comprehensive literature that systematically consolidates the biomarkers specific to CSCs in neurological cancers. Therefore, this review provides a comprehensive summary of cancer stem cell (CSC) biomarkers for neurological tumors such as glioma, meningioma, medulloblastoma and neurofibroma. It also points out the possible functions of these biomarkers in diagnosis, treatment and prognosis, providing a broader perspective. First, we quantitatively screened key words such as CSCs, biomarkers, and expression by bibliometric analysis and clarified the intrinsic connections between the key words. Then, we describe the CSC biomarkers of major neurological tumors and their pathway mechanisms, and provide an in-depth analysis of the commonalities and differences with the biomarkers of non-CSCs. In addition, many studies have shown that antipsychotic drugs can inhibit tumor growth and reduce the expression of CSC biomarkers, which facilitates targeted therapy against tumors in the nervous system. Therefore, this study will focus on the biomarkers of CSCs in the nervous system, hoping to provide guidance for future in-depth exploration and monitoring of neurological tumors for clinical applications.