Apple replant disease is a severe problem in orchards and tree nurseries. Evidence for the involvement of a nematode-microbe disease complex was reported. To search for this complex, plots with a history of apple replanting, and control plots cultivated for the first time with apple were sampled in two fields in two years. Shoot weight drastically decreased with each replanting. Nematodes were extracted from soil samples by floatation-centrifugation, washed on a 20 µm-sieve, and used for DNA extraction. Nematode communities and co-extracted fungi and bacteria were analyzed by high-throughput sequencing of amplified ribosomal fragments. The nematode community and co-extracted fungal and bacterial communities significantly differed between replanted and control plots. Free-living nematodes of the genera Aphelenchus, Cephalenchus, and an unidentified Dorylaimida were associated with replanted plots, as indicated by linear discriminant analysis effect size. Among the co-extracted fungi and bacteria, Mortierella was most indicative of replanting. Some genera, mostly Rhabditis, indicated healthy control plots. Isolating and investigating the putative disease complexes will help to understand and alleviate stress-induced root damage of apple in replanted soil.

Background: Tuberculosis (TB) manifests itself primarily in the lungs as pulmonary disease (PTB) and sometimes disseminates to other organs to cause extra-pulmonary TB, such as lymph node TB (LNTB). This study aimed to investigate the role of host genetic polymorphism in immunity related genes to find a genetic basis for such differences. Methods: Sixty-three, Single nucleotide polymorphisms (SNPs) in twenty-three, TB-immunity related genes including eleven innate immunity (SLCA11, VDR, TLR2, TLR4, TLR8, IRGM, P2RX7, LTA4H, SP110, DCSIGN and NOS2A) and twelve cytokine (TNFA, IFNG, IL2, Il12, IL18, IL1B, IL10, IL6, IL4, IL1RA, IL8 and TNFB) genes were investigated to find genetic associations in both PTB and LNTB as compared to healthy community controls. The serum cytokine levels were correlated for association with the genotypes. Results: PTB and LNTB showed differential genetic associations. The genetic variants in the cytokine genes (IFNG, IL12, IL4, TNFB and IL1RA and TLR2,4 associated with PTB susceptibility and cytokine levels but not LNTB (p < 0.05). Similarly, genetic variants in LTA4H, P2RX7, DCSIGN and SP110 showed susceptibility to LNTB and not PTB. Pathway analysis showed abundance of cytokine related variants for PTB and apoptosis related variants for LNTB. Conclusions: PTB and LNTB outcomes of TB infection have a genetic component and should be considered for any future susceptibility and functional studies.

Background: Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. Methods: We conducted summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are po-tentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed separate SMR analysis using CAGE and GTEx eQTL data. Results: Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes that were pleiotropically/potentially causally associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, PSMR=2.10×10-18), ILMN_1682717 (tagging IER3, PSMR=1.07×10-16) and ILMN_1661439 (tagging FLOT1, PSMR=1.16×10-14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, PSMR=1.44×10-10), ENSG00000186470.9 (tagging BTN3A2, PSMR=2.28×10-10), and ENSG00000224389.4 (tagging C4B, PSMR=1.23×10-9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes. Conclusion: We identified many genetic variants/loci that are potentially involved in the patho-genesis of IgAN.

Effective and sustainable water resource management requires flexibility and adaptation to local contexts. Our study analyzes how local water self-governing associations have emerged, using an adapted version of the Combined IAD-SES framework, also known as CIS. Through a compara-tive analysis of two distinct Chilean cases, the research highlights the critical role of historical factors alongside institutional support, political landscapes, and financial realities in shaping current water management practices. The findings suggest that when these elements are aligned and supportive of local water users’ associations, positive outcomes emerge, leading to more effi-cient, sustainable, and user-centered water resource management. Furthermore, this study reveals how the experiences and successes of these local user associations have shaped national policies, particularly regarding the development of monitoring mechanisms and the promotion of public-private cooperation in water governance.

Background: The COVID-19 pandemic, caused by SARS-CoV-2, has severely impacted children with Type 1 Diabetes (T1D), placing them at high risk for severe outcomes. Despite extensive observational data suggesting a link between T1D and severe COVID-19, there is a lack of understanding regarding the causal effects. This study aims to determine whether Type 1 diabetes (T1D) influences the risk of severe COVID-19 outcomes, particularly respiratory failure, in pediatric ICU patients. Methods: This two-sample Mendelian Randomization (MR) study utilized genetic data from large-scale genome-wide association studies (GWAS). Genetic variants associated with T1D were used as instrumental variables to assess their impact on severe COVID-19 outcomes. The primary datasets included genetic data for T1D from 457,695 individuals (6,447 T1D cases) and severe COVID-19 outcomes from 3,790 individuals (1,610 severe cases). Various MR methods, including Inverse Variance Weighted (IVW), MR-Egger Regression, and Weighted Median, were employed to ensure robust causal inference and eliminate potential biases such as pleiotropy. Results: The MR analysis revealed no causal relationship between T1D and severe COVID-19 outcomes in pediatric patients. Odds ratios (ORs) obtained using MR-Egger, Weighted Median, and IVW methods varied from 0.7648 to 0.9853, with p-values greater than 0.05, indicating no significant causal effect. Sensitivity analyses confirmed the robustness of these findings, showing no evidence of heterogeneity or directional pleiotropy. Conclusion: Our findings indicate no genetic causal link between T1D and severe respiratory failure from COVID-19 in children. These results contrast with numerous observational studies and suggest that metabolic dysfunction and other non-genetic factors should be the focus in managing this susceptible group. The insights gained are crucial for developing targeted interventions and improving clinical management strategies for pediatric ICU patients with T1D during the COVID-19 pandemic.

This study delves into the essential characteristics of the Metaverse and its impact on the real world. The Metaverse, a parallel universe composed of countless 3D virtual worlds, has become a hot topic in technology. With the development of advanced technologies such as Virtual Reality (VR), Augmented Reality (AR), blockchain, and Artificial Intelligence, the Metaverse is gradually changing people's lifestyles and work methods, expanding the boundaries of the digital economy. This study profoundly investigates the attributes of the Metaverse, such as asset digitization, application scenario-based, scope economy, industry universality, integrated innovation, and the platform ecosystem. Additionally, we propose a theoretical framework to aid in understanding and shaping this emerging field. We conducted a thorough study and analysis of a specific case, explored the key features of the Metaverse, unveiled the profound impact of Metaverse characteristics on social and economic development, and verified our theoretical framework with this case. This research provides new perspectives and methods for academic studies and offers robust support and guidance for practical applications and decision-making.

Genes that affect adaptive traits have been identified, but our knowledge of the genetic basis of adaptation in a more general sense (across multiple traits) remains limited. We combined population-genomic analyses of evolve and resequence experiments, genome-wide association mapping of performance traits, and analyses of gene expression to fill this knowledge gap, and shed light on the genomics of adaptation to a marginal host (lentil) by the seed beetle Callosobruchus maculatus. Using population-genomic approaches, we detected modest parallelism in allele frequency change across replicate lines during adaptation to lentil. Mapping populations derived from each lentil-adapted line revealed a polygenic basis for two host-specific performance traits (weight and development time), which had low to modest heritabilities. We found less evidence of parallelism in genotype-phenotype associations across these lines than in allele frequency changes during the experiments. Differential gene expression caused by differences in recent evolutionary history exceeded that caused by immediate rearing host. Together, the three genomic data sets suggest that genes affecting traits other than weight and development time are likely to be the main causes of parallel evolution, and that detoxification genes (especially cytochrome P450s and beta-glucosidase) could be especially important for colonization of lentil by C. maculatus.

DNA methylation and other epigenetic factors are important in the pathogenesis of late-onset Alzheimer’s disease (LOAD). Methylenetetrahydrofolate reductase (MTHFR) gene mutations occur in most elderly patients with memory loss. MTHFR is critical for production of S-adenosyl-L-methionine (SAM), the principal methyl donor. A common mutation (1364T/T) of the cystathionine-γ-lyase (CTH) gene affects the enzyme that converts cystathionine to cysteine in the trans-sulfuration pathway causing plasma elevation of total homocysteine (tHcy) or hyperhomocysteinemia – a strong and independent risk factor for cognitive loss and AD. Other causes of hyperhomocysteinemia include aging, nutritional factors, and deficiencies of B vitamins. We emphasize the importance of supplementing vitamin B₁₂ (methylcobalamin), vitamin B₉ (folic acid), vitamin B₆ (pyridoxine), and SAM to patients in early stages of LOAD.

We discuss what we believe could be an improvement in future discussions of the ever-changing brain. We do so by distinguishing different types of brain variability and outlining methods suitable to analyse them. We argue that, when studying brain and behaviour data, classical methods such as regression analysis and more advanced approaches both aim to decompose the total variance into sensible variance components. In parallel, we argue that a distinction needs to be made between innate and acquired brain variability. For varying high-dimensional brain data, we present methods useful to extract their low-dimensional representations. Finally, to trace potential causes and predict plausible consequences of brain variability, we discuss how to combine statistical principles and neurobiological insights to make associative, explanatory, predictive, and causal enquires; but cautions are needed to raise association- or prediction-based neurobiological findings to causal claims.

The electronic cigarette or e-cigarette is the new alternative method to deliver nicotine. In 2007, the United States introduced vaping devices on the market. Due to their attractive taste and lack of adverse effects, these vaping devices have gained popularity among youth and adults. However, there has been increasing evidence that vaping can cause severe lung injury, and these devices release toxic chemicals into the body. E-cigarettes are recognized as a global threat to public health. Nicotine, the most prevalent chemical in vaping devices, is associated with neurological problems and cognitive decline. Long-term use of e-cigarettes can cause adverse health problems, including respiratory illnesses, asthma, bronchitis, and lung inflammation. In 2019, the Centers for Disease Control and Prevention (CDC) officially declared vaping-associated illness EVALI (e-cigarette or Vaping Product Associated Lung Injury). The US Surgeon General published an alarm in December 2016 that e-cigarette consumption among adolescents and teens in the US is a "major public health concern" and that more young people are now vaping than smoking traditional cigarettes and cigars. Since its commercial introduction in 2003, modern electronic cigarettes (or E-cigarette s) have become increasingly popular with smokers, non-smokers, and those who used to smoke who have switched to them. With such a rise in consumption, people are becoming more aware of the irritating, harmful, and perhaps cancer-causing effects on the lungs. (Chadi et al., 2019; Dinardo & Rome, 2019; Lyzwinski et al., 2022; Marrocco et al., 2022; O’Callaghan et al., 2022)

Background: Acceleration of mass vaccination strategies is the only pathway to overcome the COVID-19 pandemic. Healthcare professionals and students have a key role in shaping public opinion about vaccines. This study aimed to evaluate the attitudes of dental students globally towards COVID-19 vaccines and explore the potential drivers for students' acceptance levels; Methods: A global cross-sectional study was carried out in February 2021 using an online ques-tionnaire. The study was liaised by the scientific committee of the International Association of Dental Students (IADS), and data was collected through the national and local coordinators of IADS member organizations. The dependent variable was the willingness to take the COVID-19 vaccine, and the independent variables included demographic characteristics, COVID-19-related experi-ence, and the drivers of COVID-19 vaccine-related attitude suggested by the WHO-SAGE; Results: A total of 6639 students from 22 countries representing all world regions responded to the ques-tionnaire properly. Their mean age was 22.06 ± 2.79 (17-40) years, and the majority were females (70.5%), in clinical years (66.8%), and from upper-middle-income economies (45.7%). In general, 22.5% of dental students worldwide were hesitant, and 13.9% rejected COVID-19 vaccines. The students in low- and lower-middle-income (LLMI) economies had significantly higher levels of vaccine hesitancy compared to their peers in upper-middle- and high-income (UMHI) economies (30.4% vs 19.8%; p < 0.001); Conclusions: The global acceptance level of dental students for COVID-19 vaccines was suboptimal, and their worrisome level of vaccine hesitancy was influenced by the socioeconomic context where the dental students live and study. The media and social media, public figures, insufficient knowledge about vaccines, and mistrust of governments and the pharmaceutical industry were barriers to vaccination. The findings of this study call for further implementation of epidemiology (infectious diseases) education within undergraduate dental curricula.

Evidence-based medicine (EBM) is in crisis, in part due to bad methods, which are understood as misuse of statistics that is considered correct in itself. This article exposes two related common misconceptions in statistics, the effect size (ES) based on correlation (CBES) and a misconception of contingency tables (MCT). CBES is a fallacy based on misunderstanding of correlation and ES and confusion with 2 × 2 tables, which makes no distinction between gross crosstabs (GCTs) and contingency tables (CTs). This leads to misapplication of Pearson’s Phi, designed for CTs, to GCTs and confusion of the resulting gross Pearson Phi, or mean-square effect half-size, with the implied Pearson mean square contingency coefficient. Generalizing this binary fallacy to continuous data and the correlation in general (Pearson’s r) resulted in flawed equations directly expressing ES in terms of the correlation coefficient, which is impossible without including covariance, so these equations and the whole CBES concept are fundamentally wrong. MCT is a series of related misconceptions due to confusion with 2 × 2 tables and misapplication of related statistics. The misconceptions are threatening because most of the findings from contingency tables, including CBES-based meta-analyses, can be misleading. Problems arising from these fallacies are discussed and the necessary changes to the corpus of statistics are proposed resolving the problem of correlation and ES in paired binary data. Since exposing these fallacies casts doubt on the reliability of the statistical foundations of EBM in general, we urgently need to revise them.