Collaboration between researchers has been shown to influence their productivity and scientific impact. Although these ties have been widely discussed in the literature, the nature of the co-authorship networks between the most successful scholars remains a question. To provide an answer, this study conducts a cross-case analysis of the collaboration networks between Highly Cited Researchers, focusing on the research output and co-authorship patterns in Highly Cited Papers across three award categories: Clinical Medicine, Materials Science, and Social Sciences. Our findings indicate that there are category-specific differences in publication output and the intensity of collaboration between Highly Cited Researchers. Notably, Highly Cited Researchers in the Social Sciences demonstrate a less collaborative approach to research than those in Clinical Medicine and Materials Science. While Highly Cited Researchers in all three categories featured interconnected collaboration networks among themselves, those in Clinical Medicine and Materials Science exhibited a more collaborative environment, while those in Social Sciences showed a tendency towards independent research efforts. The case of Social Sciences is further evidenced by higher fragmentation within the collaboration network of Social Sciences, indicating a less cohesive collaborative framework. The analysis of the Giant Component—the largest cohesive subset of the network—revealed that it is less representative of the overall network structure in the Social Sciences than in Clinical Medicine and Materials Science. Finally, the centrality measures indicated that Highly Cited Researchers with high betweenness and closeness centrality act as crucial bridges within each network, significantly shaping the structural cohesion and collaborative dynamics of their respective fields.

The present study builds upon the authors′ previous research, which highlighted the fuel consumption advantage of LNG-powered (liquefied natural gas) trucks over conventional diesel vehicles. Expanding on this topic, the aim of this research is to analyze the influence of static and dynamic driving factors on the consumption advantage of LNG vehicles. The study was conducted in a test-track environment, ensuring optimal reproducibility with minimal external influencing factors, allowing for various types of measurements. In this research, fuel consumption values were recorded indirectly through the fleet management system (FMS) using controller area network (CAN) messages. Data distribution analysis, the Shapiro-Wilk test, and ANOVA were employed to validate the research hypotheses. Our study is unique in the field of heavy-duty vehicles (HDVs) as the measurements were performed at the test-track level, providing precise data for emission differences. The results indicate that the static driving environment (represented by different test track modules) has a stronger influence on the consumption advantage of LNG vehicles. In contrast, driving mode has a lesser effect on the consumption difference between LNG and diesel trucks.

Background: In recent years, comprehensive analyses using a genome-wide association study (GWAS) have been conducted to identify genetic factors related to athletic performance. In this study, we investigated the association between genetic variants and elite wrestling status across multiple ethnic groups using a genome-wide genotyping approach. Methods: This study included 168 elite wrestlers (64 Japanese, 67 Turkish, and 36 Russian), all of whom had competed in international tournaments, including the Olympic Games. Control groups consisted of 306 Japanese, 137 Turkish, and 173 Russian individuals without elite athletic backgrounds. We performed a GWAS comparing allele frequencies of single-nucleotide polymorphisms (SNPs) between elite wrestlers and controls in each ethnic cohort. Cross-population analysis comprised (1) identifying SNPs with nominal significance (p < 0.05) in all three groups, then (2) meta-analyzing overlapped SNPs to assess effect consistency and combined significance. Finally, we investigated whether the most significant SNPs were associated with gene expression in skeletal muscle in 23 physically active men. Results: The GWAS identified 328,388 (Japanese), 23,932 (Turkish), and 30,385 (Russian) SNPs reaching nominal significance. Meta-analysis revealed that the ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms were associated (p < 0.0001) with elite wrestling status across all three populations. Both variants are located in intronic regions and influence the expression of their respective genes in skeletal muscle. Conclusions: This is the first study to investigate gene polymorphisms associated with elite wrestling status in a multi-ethnic cohort. ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms may represent important genetic factors associated with achieving an elite status in wrestling, irrespective of ethnicity.

An assessment of the air quality of Győr (Hungary) was performed by determining the ambient concentrations of PM10, PM10-bound polycyclic aromatic hydrocarbons (PAHs) and regulated heavy metals (Pb, Cd, As and Ni) in the period of 2008−2012. The PM10 concentrations ranged from 7.90 to 119.14 μg/m³ with the mean value of 34.94 μg/m³. On average, the total PAHs (sum of the concentrations of 18 individual PAH compounds) and the four metals contents in the PM10 fraction amounted to 0.04 and 0.06 %, respectively. The total PAH concentrations ranged from 0.29 to 88.30 ng/m³, which were predominated by intermediate and high molecular weight PAHs. Higher concentrations of both PM10 and PAHs were detected in samples collected in the heating seasons. The mean metallic concentrations calculated for the 5-year sampling period were found in decreasing order of Pb (14.47 ng/m³), Ni (3.73 ng/m³), As (0.64 ng/m³) and Cd (0.60 ng/m³). Very little seasonal variation was observed in metal concentrations. Moreover, the comparison of the PM10, bezo[a]pyrene (BaP) and heavy metal concentrations determined with other Hungarian and European urban sites and the limit or target values for health protection has revealed that the air quality of Győr and other Hungarian cities for the above pollutants generally corresponds to the EU average. It is important to reduce PM10 and BaP pollution to concentrations lower than those specified in current legislation. However, the Hungarian cities have excellent air quality with respect to heavy metals.

Insulin-producing β-cells are destroyed in type 1 diabetes mellitus (T1DM), a chronic autoimmune disease that results in complete insulin insufficiency and metabolic dysfunction. According to a survival study that used p values, some hub genes are important for predicting and diagnosing illness. Scientists have inferred medicines to identify possible therapies that interact with the identified hub genes. The GSE10586 gene expression dataset from the Gene Expression Omnibus (GEO) was used for this investigation, which included 27 samples from 15 healthy controls and 12 diabetic patients. Normalization methods such as variance stabilization normalization (VSN) were used as part of the data pretreatment. A protein‒protein interaction (PPI) network was constructed, principal component analysis (PCA) was performed, heatmaps were created, and the Limma algorithm was used to analyze differential gene expression. Using DAVID v6.8 and KEGG pathway annotations, the functional enrichment of differentially expressed genes (DEGs) was evaluated. Furthermore, a computational study revealed CERS6 to be one of the potential hub genes. Four drugs, methotrexate, eliglustat, myriocin and statin, were the focus of further studies on the basis of predictions made via ChemSpider and PubChem database analysis. To determine the optimal binding positions of these drugs with CERS6, we used molecular docking techniques. The binding affinity of methotrexate was 8.48 kcal/mol, that of myriocin was 7.85 kcal/mol, that of eliglustat was − 6.62 kcal/mol, and that of serine was − 4.90 kcal/mol against the binding pocket’s active residues. To determine how consistently each drug interacted with the CERS6 protein over time, molecular dynamics (MD) simulations were run. Throughout the simulation intervals, both medications were confirmed to be stable, with minor alterations in the CERS6 protein loop region. Therefore, the investigation of structure-based drug design has potential for identifying specific therapeutic targets. Ten hub genes were identified via network analysis of differentially expressed genes. These hub genes could serve as novel targets for T1DM detection, prognosis, and targeting. CERS6 exhibited the highest degree of interaction. Methotrexate, eliglustat, myriocin and statins were identified as potential drugs for CERS6. Overall, these findings provide valuable insights that could pave the way for new experimental strategies in T1DM therapy.

Background: Cancer is a leading cause of death globally. Accurate cancer burden information is crucial for policy planning, but many countries do not have up-to-date cancer surveillance data. To inform global cancer-control efforts, we used the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework to generate and analyse estimates of cancer burden for 47 cancer types or groupings by age, sex, and 204 countries and territories from 1990 to 2023, cancer burden attributable to selected risk factors from 1990 to 2023, and forecasted cancer burden up to 2050. Methods: Cancer estimation in GBD 2023 used data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Cancer mortality was estimated using ensemble models, with incidence informed by mortality estimates and mortality-to-incidence ratios (MIRs). Prevalence estimates were generated from modelled survival estimates, then multiplied by disability weights to estimate years lived with disability (YLDs). Years of life lost (YLLs) were estimated by multiplying age-specific cancer deaths by the GBD standard life expectancy at the age of death. Disability-adjusted life-years (DALYs) were calculated as the sum of YLLs and YLDs. We used the GBD 2023 comparative risk assessment framework to estimate cancer burden attributable to 44 behavioural, environmental and occupational, and metabolic risk factors. To forecast cancer burden from 2024 to 2050, we used the GBD 2023 forecasting framework, which included forecasts of relevant risk factor exposures and used Socio-demographic Index as a covariate for forecasting the proportion of each cancer not affected by these risk factors. Progress towards the UN Sustainable Development Goal (SDG) target 3.4 aim to reduce non-communicable disease mortality by a third between 2015 and 2030 was estimated for cancer. Findings: In 2023, excluding non-melanoma skin cancers, there were 18·5 million (95% uncertainty interval 16·4 to 20·7) incident cases of cancer and 10·4 million (9·65 to 10·9) deaths, contributing to 271 million (255 to 285) DALYs globally. Of these, 57·9% (56·1 to 59·8) of incident cases and 65·8% (64·3 to 67·6) of cancer deaths occurred in low-income to upper-middle-income countries based on World Bank income group classifications. Cancer was the second leading cause of deaths globally in 2023 after cardiovascular diseases. There were 4·33 million (3·85 to 4·78) risk-attributable cancer deaths globally in 2023, comprising 41·7% (37·8 to 45·4) of all cancer deaths. Risk-attributable cancer deaths increased by 72·3% (57·1 to 86·8) from 1990 to 2023, whereas overall global cancer deaths increased by 74·3% (62·2 to 86·2) over the same period. The reference forecasts (the most likely future) estimate that in 2050 there will be 30·5 million (22·9 to 38·9) cases and 18·6 million (15·6 to 21·5) deaths from cancer globally, 60·7% (41·9 to 80·6) and 74·5% (50·1 to 104·2) increases from 2024, respectively. These forecasted increases in deaths are greater in low-income and middle-income countries (90·6% [61·0 to 127·0]) compared with high-income countries (42·8% [28·3 to 58·6]). Most of these increases are likely due to demographic changes, as age-standardised death rates are forecast to change by –5·6% (–12·8 to 4·6) between 2024 and 2050 globally. Between 2015 and 2030, the probability of dying due to cancer between the ages of 30 years and 70 years was forecasted to have a relative decrease of 6·5% (3·2 to 10·3). Interpretation: Cancer is a major contributor to global disease burden, with increasing numbers of cases and deaths forecasted up to 2050 and a disproportionate growth in burden in countries with scarce resources. The decline in age-standardised mortality rates from cancer is encouraging but insufficient to meet the SDG target set for 2030. Effectively and sustainably addressing cancer burden globally will require comprehensive national and international efforts that consider health systems and context in the development and implementation of cancer-control strategies across the continuum of prevention, diagnosis, and treatment. Funding: Gates Foundation, St Jude Children's Research Hospital, and St Baldrick's Foundation.

This review investigates genetic strategies aimed at improving robustness in pigs to enhance disease resistance and reduce reliance on antibiotics. Robustness refers to a pig’s ability to stay healthy and productive under stressful or challenging conditions. The review outlines current breeding practices focused on key traits such as maternal ability, growth, immune function, and survival, and highlights that these robustness-related traits show measurable heritability, making them suitable for genetic improvement. Special attention is given to resistance against porcine reproductive and respiratory syndrome (PRRS), a major disease in swine. We also evaluate breed-specific differences, environmental influences, and immune response profiles, emphasizing their impact on breeding outcomes. The development of robust pig lines emerges as a sustainable strategy to reduce antibiotic dependence and enhance herd health. A distinctive contribution of this work is the integration of genetic robustness and resilience strategies with antibiotic stewardship objectives. We link genomic selection, advanced phenotyping, and targeted management interventions within a One Health framework to outline actionable, system-level pathways for reducing antimicrobial inputs. To our knowledge, this combined genetic and public health perspective has not been comprehensively addressed previously.

The reuse of polluted drainage water for irrigation is increasingly unavoidable in arid and semi-arid regions, yet it poses serious risks due to the accumulation of toxic heavy metals in soils and crops. Although biochar and plant growth-promoting rhizobacteria (PGPR) have individually shown potential to alleviate metal stress, field-scale evidence elucidating their synergistic and mechanistic effects under realistic, combined soil- and irrigation-derived contamination remains limited. This study addresses this gap by evaluating the effectiveness of PGPR-enriched biochar in mitigating lead (Pb), cadmium (Cd), and nickel (Ni) stress in canola (Brassica napus L.) grown under open-field conditions. A naturally contaminated clay soil was continuously irrigated with polluted drainage water from the Kitchener drain (Egypt), creating chronic heavy metal stress. Biochar was applied at 5 and 10 ton ha⁻¹, alone or enriched with defined PGPR consortia composed of Bacillus circulans NCAIM B.02324, Azospirillum brasiliense SARS 1001, and Pseudomonas koreensis MG209738, applied via seed inoculation. The combined application of 10 ton ha⁻¹ biochar with the three-strain consortium (10BC+PGPR3) produced the strongest responses. This treatment (10BC+PGPR3) significantly enhanced soil microbial respiration and key enzyme activities, indicating improved soil biological functioning, while reducing extractable Pb, Cd, and Ni by 55–65% relative to the untreated control. These soil-level improvements translated into marked reductions in metal uptake and translocation to shoots and seeds, alongside enhanced plant water status, membrane stability, and oxidative stress tolerance. Consequently, seed yield and oil content increased by ~ 60% and ~ 90%, respectively. Overall, this study demonstrates that PGPR-enriched biochar acts through coupled soil biochemical and plant physiological mechanisms to immobilize heavy metals and restore crop productivity under real contaminated irrigation scenarios. The findings provide robust field-based evidence supporting this integrated strategy as a practical and sustainable solution for improving soil health, crop performance, and food safety in heavy metal-affected agroecosystems.

Young employees have to make many trade-offs in their job search and are often confronted with a need for more skills and abilities. In our survey, we investigated the expectations of young employees towards employers about their level of education and the factors that influence/inhibit their job placement. The results confirmed that remuneration, motivation, an inspiring work environment, and career opportunities offered by employers play a significant role in job choice. The young employees' responses showed that they feel underprepared for the challenges of the labor market and see the solution to this problem in increasing the number of practical training hours. The study highlights the differences in the expectations of students at different levels of education and could be a starting point for further research.