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Examining the bacterial diversity including extracellular vesicles in air and soil: implications for human health
As the significance of human health continues to rise, the microbiome has shifted its focus from microbial composition to the functional roles it plays. In parallel, interest in ultrafine particles associated with clinically important impact has been increasing. Bacterial extracellular vesicles (BEVs), involved in systemic microbiome activity, are nano-sized spherical vesicles (20 - 100 nm in diameter) containing DNA, RNA, proteins, and lipids. They are known to be absorbed into the body potentially through air and soil, circulate in the blood, and directly impact diseases by affecting organs. Therefore, the aim of this study is to examine the biodiversity of bacteria and BEVs and predicted functional pathways. We sampled air and soil samples in Seoul, Korea and analyzed metagenomics based on 16S rRNA sequencing. At the phylum levels, Firmicutes in BEVs from soil and air were significantly higher than in bacteria, and Acidobacteria in both bacteria and BEVs from soil were significantly higher than from air (p < 0.05). The most dominant genera were Pseudomonas in bacteria from air and soil; and Escherichia–Shigella in BEVs from air and soil. In addition, Two-component system (ko02020) and ATP-binding cassette transporters (ko02010) were dominant functional pathways in both air and soil. The most functional pathways and orthologous groups were significantly different between air and soil (p < 0.05). In conclusion, human health can be affected differently depending on type of environment. Future study is necessary to have a better understanding of human health effects from environmental microbiota.
Incidence and predictors of type 2 diabetes mellitus during 17 years of follow-up in the Golestan Cohort Study
O-GlcNAcylation modification of MyoD regulates skeletal muscle fiber differentiation by antagonizing the UPF1 pathway
Transcriptomic profile induced by calcitriol in CaSki human cervical cancer cell line
The vitamin D endocrine system, primarily mediated by its main metabolite calcitriol and the vitamin D receptor (VDR), plays a critical role in numerous human physiological processes, ranging from calcium metabolism to the prevention of various tumors, including cervical cancer. In this study, we comprehensively investigated the genomic regulatory effects of calcitriol in a cervical cancer model. We examined the transcriptional changes induced by calcitriol in CaSki cells, a cervical cell line harboring multiple copies of HPV16, the primary causal agent of cervical cancer. Our microarray findings, revealed that calcitriol regulated over 1000 protein-coding genes, exhibiting a predominantly repressive effect on the CaSki cell transcriptome by suppressing twice as many genes as it induced. Calcitriol decreased EPHA2 and RARA expression while inducing KLK6 and CYP4F3 expression in CaSki cells, as validated by qPCR and Western blot. Functional analysis demonstrated that calcitriol effectively inhibited key processes involved in cancer progression, including cell proliferation and migration. This was further supported by the significant downregulation of MMP7 and MMP13 mRNA levels. Our microarray results also showed that, in addition to its effects on protein-coding genes, calcitriol significantly regulates non-coding RNAs, altering the expression of approximately 400 non-coding RNAs, including 111 microRNA precursors and 29 mature microRNAs, of which 17 were upregulated and 12 downregulated. Notably, among these calcitriol-regulated microRNAs are some involved in cervical cancer biology, such as miR-6129, miR-382, miR-655, miR-211, miR-590, miR-130a, miR-301a, and miR-1252. Collectively, these findings suggest that calcitriol exhibits a significant antitumor effect in this advanced cervical cancer model by blocking critical processes for tumor progression, underscoring the importance of maintaining adequate vitamin D nutritional status.
Multiple domain resilience components and frailty, postoperative complications, and one year quality of life deterioration after pancreatectomy in older patients
Structure-function studies of a nucleoplasmin isoform from Plasmodium falciparum
Construction of cDNA library of Dalbergia odorifera induced by low temperature stress and screening of low temperature tolerant genes
To systematically analyze the gene function of Dalbergia odorifera, the seedlings of D. odorifera were treated with low-temperature stress for 6 h. Total RNA was extracted from a mixture of seedling roots, stems, and leaves, and a low-temperature-induced D. odorifera yeast cDNA expression library was constructed. The library volume was 1.032 × 108 CFU, and the PCR (Polymerase Chain Reaction) identification of the library bacterial fluid showed that the amplification was around 1000 bp, with a single randomly distributed band, indicating that the library had been recombinantly inserted into the pYES2 vector. The GO (Gene Ontology) analysis showed that the library genes were mainly involved in metabolic and stress signaling pathways. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis showed that the genes were primarily related to energy and metabolic pathways. Twenty-one genes were screened or obtained at -20°C for low-temperature tolerance. In addition, the organ expression profiles of the candidate genes were analyzed based on RNA-seq data, and the expression profiles of the candidate genes under low-temperature stress were also examined. The construction of the yeast library provides genetic resources for the analysis of the mechanism of low-temperature tolerance of D. odorifera, which is important for comprehending and utilizing the genetic resources of D. odorifera.
Author Correction: Kyphoplasty with intravertebral reduction devices associated with better height restoration and greater kyphosis correction than kyphoplasty with balloons
An alternative adaptation strategy of the CCA-adding enzyme to accept noncanonical tRNA substrates in Ascaris suum
Correction: Beyond ingredients: Supramolecular structure of lipid droplets in infant formula affects metabolic and brain function in mouse models
A novel analytical approach to design horizontal well completion using ICDs to eliminate heel-toe effect
Requirements for nuclear GRP78 transcriptional regulatory activities and interaction with nuclear GRP94
Correction: Relationships between multivitamins, blood biochemistry markers, and BMC and BMD based on RF: A cross-sectional and population-based study of NHANES, 2017–2018
The study of miR-130a expression and its mechanism of action in peripheral blood endothelial progenitor cells (EPCs) in type 2 diabetes mellitus (T2DM)
Structural insight into the catalytic mechanism of the bifunctional enzyme l-fucokinase/GDP-fucose pyrophosphorylase
Unveiling the psychological traits of multi-marathoners: Insights from TIPI personality trait analysis
Objectives Multi-marathoners, athletes dedicated to completing 100 + marathons, represent a unique endurance sport subculture. This study examines their psychological traits using the Ten Item Personality Inventory (TIPI) and Latent Class Analysis (LCA) to identify personality-based profiles and subgroup differences. Methods An online cross-sectional survey of 593 multi-marathoners (56% men, 44% women, mean age = 53.87, SD = 9.91, countries = 22) collected TIPI data. Reliability was assessed using Cronbach’s Alpha and Guttman’s Lambda 6. Statistical analyses included Mann-Whitney U tests, ANOVA Aligned Rank Transform (ART), Wilcoxon post-hoc tests, and Spearman’s correlations to examine personality differences across gender, age and health variables. LCA identified distinct personality subgroups. Normative TIPI data served as a comparison benchmark. Results Multi-marathoners exhibited higher conscientiousness (F(1,591) = 2.42, p < 0.001) but lower emotional stability (F(1,591) = 5.525, p < 0.001) than the general population, suggesting strong goal-directed behaviour but challenges in stress management. Women showed higher agreeableness (W = 50809, p < 0.00091), while age-related differences were not statistically significant. LCA revealed four personality-based subgroups, including those with high resilience and others with health vulnerabilities. Conclusion Multi-marathoners display distinct psychological traits, particularly high conscientiousness and lower emotional stability. These findings highlight the need for tailored psychological interventions to support multi-marathon athletes' participation and well-being. Future research should explore longitudinal patterns and explore the efficacy of psychological interventions to enhance participation and well-being.
Prevalence and determinants of neonatal infections in Benin based on a retrospective study in six reference hospitals
The structural basis of TRIM25-mediated regulation of RIG-I
Can we use lower extremity joint moments predicted by the artificial intelligence model during walking in patients with cerebral palsy in the clinical gait analysis?
Several studies have highlighted the advantages of employing artificial intelligence (AI) models in gait analysis. However, the credibility and practicality of integrating these models into clinical gait routines remain uncertain. This study critically evaluates an AI model’s ability to predict lower extremity joint moments during gait in patients with cerebral palsy (CP). We employed a three-step approach to assess the feasibility of a previously developed AI model that predicted joint moments during walking for 622 patients with CP, using joint kinematics as input. First, we established clinically relevant thresholds for lower extremity joint moments, categorizing into three labels: acceptable (Green), acceptable with caution (Yellow), and unacceptable (Red). This categorization was based on the normalized root mean square error (nRMSE) between lab-measured and predicted joint moments. We explored the relationship between gait kinematics and joint moments by correlating the kinematic inputs with their respective output labels. Finally, we developed a linear discrimination analysis (LDA) model to predict labels for newly predicted joint. Assessing the validity of thresholds, an ANOVA one-way analysis and Bonferroni post-hoc statistical tests were performed to find significant differences between the nRMSE values for each label. The hip joint exhibited the largest population of Green labels (84%), while the ankle joint had the smallest (50%). Regressive differences in joint kinematics and gait profile scores were observed across all labels. The LDA model achieved an accuracy of 85.2% and an F-score of 92% for predicting Green label in hip joint moment. Additionally, more severe patient conditions were associated with an increase in Red-labeled predictions. Our findings highlight significant differences in nRMSE among labels, demonstrating the effectiveness of the proposed thresholds for labeling joint moments. Overall, the AI model’s performance was rated as moderate, and the three-step approach proved valuable for assessing the feasibility of AI models in clinical settings.