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Despite advances in cancer immunotherapies across various cancers, survival outcomes in colorectal cancer (CRC) with these agents remain largely unsatisfactory despite the high tumour burden. Colorectal stem cells (CSCs), especially LGR5+ CSCs, are the significant drivers in CRC initiation, progression and resistance to conventional therapies. Although native immune surveillance is sufficient to combat early tumour formation, CRC evades early immune detection with its well-documented adenoma-to-carcinoma sequence. The exact mechanism underlying this phenomenon still needs to be better understood. SRY-related HMG box gene 17 (SOX17), a transcription factor that specifies embryonic gut formation, is increasingly recognised as a significant factor in CRC tumourigenesis. However, its role as a tumour suppressor or oncogene is still debated. Evidence from a recent study highlighted the critical role of SOX17 in reshaping the tumour immune ecosystem through the simultaneous inhibition of CD8+ T cells and selective suppression of LGR5 expression in CSCs through transcriptional repression, thereby facilitating disease progression. Given its role in immune evasion, SOX17 could be a promising marker in personalised therapy. Additionally, SOX17 could play a role in the diagnostic arena, potentially identifying dysplasia in the gastrointestinal tract. Future clinical, basic and genetic studies focusing on SOX17 are needed to ascertain its mechanistic role in tumour immunomodulation in CRC and diagnosing preneoplastic lesions in the gastrointestinal tract.

Objectives: To determine the overall effect size, identify the study with the strongest effect size, and examine the age group with the strongest relationships between the variables. Methods: In this study, a meta-analytical analysis was carried out by bringing together 13 studies from around the world examining the statistical relationships between cardiothoracic ratio (CTR) and left ventricular ejection fraction (LVEF). Thus, it is hoped that the results will contribute to studies on the relationships between CTR and LVEF and bring a holistic view to these relationships. To determine CTR, studies were identified through a review of the literature, and those that reported a correlation between the variables under investigation were included in the analysis process. The date range of this study 01.11.2022-15.01.2023. Results: According to the findings, when all the results were analysed together, the mean effect size for CTR and LVEF correlation was found to be r=-0.12. When all studies were considered separately, generally small negative correlations were observed between CTR and LVEF. It is possible to say that there is no publication bias in the studies. Conclusion: This study is a meta-analytic study combining 13 studies examining the statistical relationships between CTR and LVEF. The results of this study are expected to make a valuable contribution to the field of research on the relationship between CTR and LVEF, providing a more comprehensive understanding of these associations. PROSPERO Reg. No.: 392207

Sinonasal papillomas are benign epithelial tumors of the sinonasal tract that are associated with a synchronous or metachronous sinonasal carcinoma in a subset of cases. Our group recently identified mutually exclusive EGFR mutations and human papillomavirus (HPV) infection in inverted sinonasal papillomas and frequent KRAS mutations in oncocytic sinonasal papillomas. We also demonstrated concordant mutational and HPV infection status in sinonasal papilloma-associated sinonasal carcinomas, confirming a clonal relationship between these tumors. Despite our emerging understanding of the oncogenic mechanisms driving formation of sinonasal papillomas, little is currently known about the molecular mechanisms of malignant progression to sinonasal carcinoma. In the present study, we utilized targeted next-generation DNA sequencing to characterize the molecular landscape of a large cohort of sinonasal papilloma-associated sinonasal carcinomas. As expected, EGFR or KRAS mutations were present in the vast majority of tumors. In addition, highly recurrent TP53 mutations, CDKN2A mutations, and/or CDKN2A copy-number losses were detected; overall, nearly all tumors (n = 28/29; 96.6%) harbored at least one TP53 or CDKN2A alteration. TERT copy-number gains also occurred frequently (27.6%); however, no TERT promoter mutations were identified. Other recurrent molecular alterations included NFE2L2 and PIK3CA mutations and SOX2, CCND1, MYC, FGFR1, and EGFR copy-number gains. Importantly, TP53 mutations and CDKN2A alterations were not detected in matched sinonasal papillomas, suggesting that these molecular events are associated with malignant transformation. Compared to aerodigestive tract squamous cell carcinomas from The Cancer Genome Atlas (TCGA) project, sinonasal papilloma-associated sinonasal carcinomas have a distinct molecular phenotype, including more frequent EGFR, KRAS, and CDKN2A mutations, TERT copy-number gains, and low-risk human papillomavirus (HPV) infection. These findings shed light on the molecular mechanisms of malignant progression of sinonasal papillomas and may have important diagnostic and therapeutic implications for patients with advanced sinonasal cancer.

Background This study conducts a bibliometric analysis of global trends in ‘artificial intelligence studies in veterinary medicine’. The analysis aims to summarise the publications of researchers from various disciplines related to artificial intelligence in veterinary medicine, thereby predicting future trends of AI in this field. The primary objective of the study is to investigate publications pertaining to artificial intelligence in veterinary medicine worldwide and to analyse trends and future developments in this area. Methods This bibliometric study examines artificial intelligence research in veterinary medicine conducted worldwide from 1990 to 2024. To achieve this, a search using the keywords ‘artificial intelligence’ and ‘veterinary medicine’ was performed in the Web of Science (WOS) database, resulting in the identification of 1497 studies. After excluding irrelevant publications and those outside the scope of articles, a total of 1400 articles were included in the analysis. The data collection process utilised titles, author names, publication years, journal names, and citation counts. All textual data were analysed using VOSviewer software to ensure accuracy and reliability. In this study, analyses conducted through text mining and data visualisation techniques (e.g., bubble maps) facilitated a clearer understanding of the results. Results This study presents information about 1400 articles obtained from the WOS database and a total of 44,700 citations for these articles. The average number of citations per article is 32, with an H‐index of 74. A rapid increase in both the number of articles and citations has been observed since 2019. The majority of the articles (30%) were published in the fields of veterinary sciences, artificial intelligence, and computer sciences. The United States, Taiwan and the United Kingdom are the leading countries, accounting for 84% of the published articles in this field. Additionally, 12% of the articles were published in the area of veterinary sciences, and 85% of the articles fall within the SCI‐Expanded category. Conclusions The findings of our study indicate that there are numerous active researchers in the field of artificial intelligence in veterinary medicine and that research in this area is steadily increasing. This bibliometric analysis highlights global trends and significant works in artificial intelligence within veterinary medicine, providing valuable insights into the future directions of research in this field. As the analysis aims solely to identify trends and patterns in the literature, it does not intend to evaluate the applicability of the subject matter. Highlights Analysis of Global Trends: This study comprehensively analyses the global trends and effects of research on artificial intelligence in veterinary medicine. In this context, it contributes to the identification of significant changes and developments in the literature. Rapidly Spreading Research: Research on artificial intelligence in veterinary medicine has rapidly expanded in recent years, and this trend is expected to continue. The increase in studies indicates an expansion of knowledge and applications in this field. Diagnostic and Therapeutic Tools: Artificial intelligence research serves as a valuable tool in veterinary medicine, particularly in improving the diagnosis and treatment processes for various diseases. This contributes to the development of more effective methods for animal health and care. Increasing Number of Publications: The number of studies on artificial intelligence in veterinary medicine worldwide is increasing each year. Notably, after the Covid‐19 pandemic, there has been a significant rise in publications in this field. This indicates that the importance of artificial intelligence in both human and animal health has grown, with the pandemic intensifying research interest. Prominent Countries: Among the countries examined in the study, the United States, Taiwan, England, and Germany emerged as leaders in this research area. Conversely, it was noted that some countries have very few or no academic publications in the field of artificial intelligence in veterinary medicine. This study demonstrates a global increase in scientific research and publications on artificial intelligence (AI) in veterinary medicine. The analysis aims to summarise the publications of researchers from various disciplines related to AI in veterinary medicine, thereby predicting future trends of AI in this field. Furthermore, identifying key journals, authors, and notable studies points to potential areas for increased use of animal models in AI research in the future. The findings of our study indicate that there are numerous active researchers in the field of AI in veterinary medicine and that research in this area is steadily increasing. In conclusion, a better understanding of the potential of AI in veterinary medicine offers significant opportunities for improving animal health and welfare in the future.

Ongoing climate change is anticipated to shift the geographical distribution range and impact local abundance of tree species by altering their ecological conditions. Given the lower resilience of populations at the species’ range edges, locally adapted range-edge populations are critical to the species’ survival under climate change. In this context, the distribution of holm oak (Quercus ilex L.) at the eastern border of its distribution range was assessed under current, past, and foreseeable future climate change scenarios, using species distribution models (SDMs). Current SDMs were developed using WorldClim 1.4 climate data as baseline at 30-second spatial resolution by using Generalized Boosted Regression Models (GBM) and showed moderate model performance. To compare temporal transferability and account for climate uncertainties of two versions of future climate data (CMIP5 and CMIP6), we used 4 Global Circulation Models (GCMs), 2 emission scenarios (moderate RCP45/SSP245 and pessimistic - RCP85/SSP585) for 2 different periods in the future (2040-2060 and 2060-2080). We also made predictions about the past (Mid-Holocene, about 6.000 years ago) using 4 CMIP5 GCMs. Most important variables of SDMs were distance to the sea, isothermality (BIO3), annual precipitation (BIO12), the mean temperature of driest quarter (BIO9), and the precipitation of driest month (BIO14). Our findings showed that the species’ potential distribution range probably used to be much wider in the mid-Holocene, which implies that the holm oak had a broader climatic niche during this period. The future projections indicate that its distribution area in the eastern border might increase particularly in the Black Sea region, while decreasing in the Aegean region resulting in a likely northward range shift in Turkey. However, other variables not included in our models such as land use changes might drive future shifts. Due to its high resistance to dry conditions and resilience, this species might continue to spread in southwestern Turkey in 2050s and 2070s. Finally, our study fills the gap in potential distribution predictions in context of climate change for the eastern boundary of the holm oak.

The study compared the effects of three different heat stress conditions on the psycho-physiological responses and locomotor demands of young players in different small-sided soccer games (SSGs). Sixteen soccer players (age: 16.5 ± 0.5 years) performed 2-a-side and 4-a-side SSGs under three environmental heat stress conditions: low environmental heat (LEH) ≤ 23.9°C, moderate environmental heat (MEH) 24.0-27.9°C, and high environmental heat (HEH) 28.0-32.9°C. Players' heart rate (HR) responses and total distance covered (TDC) were continuously monitored for all SSGs; the rating of perceived exertion (RPE) and visual analog scale (VAS) were used after each bout. Tympanic temperature (TT) was recorded daily before and after the SSGs. The results demonstrated that for both 2-a-side and 4-a-side SSGs, significant main effects of temperature were observed for HR, %HRmax, RPE, and VAS responses (all p < 0.05), indicating progressively increased cardiovascular strain and perceptual load under higher heat stress conditions. In contrast, no significant main effects of temperature were found for TDC and TT responses in either game format. Interaction analyses revealed significant temperature × bout effects for most psychophysiological variables in both SSG formats, including HR, %HRmax, RPE, and VAS responses (all p < 0.05), suggesting that responses to heat stress varied across repeated bouts. However, no significant interaction effects were observed for TDC or TT in the 4-a-side SSGs, while only limited interaction effects emerged for TDC in the 2-a-side SSGs format. Overall, the findings indicate that heat stress substantially amplifies cardiovascular and perceptual responses during SSGs, with effects modulated by game format and bout structure. In contrast, TDC and TT appear less sensitive to these conditions. Coaches may use this evidence to manage players' internal and external load and optimize their team's performance across various heat-stress conditions.

In wireless communications the demand for wide range of services is leading to a rapid increase in network performance requirements. Hence, today’s cellular radio technologies are designed to operate closer to Shannon capacity bound which sets the ultimate upper limit for the wireless channel capacity. Yet, good link level performance does not necessarily mean that network resources are used efficiently as the cellular capacity and coverage performance may not be optimal resulting from dynamic conditions in radio network environment such as urbanization, insertion or deletion of base stations, and malfunctioning nodes. Due to the fact that reacting on those inherent problems manually is very expensive and time consuming, automated optimization of cellular coverage and capacity by means of self-optimization of adaptive antenna system parameters could be an attractive solution from the network operator’s point of view. Furthermore, suboptimal antenna parameter selection in long term evolution (LTE) network planning or the reuse of the sites and antenna parameters of a preceding access technology requires optimization of adaptive antenna system parameters. In this article we propose a novel centralized self-optimization approach that can be used for adapting antenna system parameters in order to automatically control network capacity and coverage in a macro-cellular deployment. In the proposed approach we present case-based reasoning (CBR) based self-optimization aided by an exemplary rule-based scheme which is required during the training phase of CBR. Dynamic system level downlink simulator is developed to validate the performance of the proposed approach in a realistic macro-cellular scenario. In performance evaluations the 3 rd generation partnership project LTE system framework is assumed and propagation is modeled in three dimensions.

The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gönen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m × 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 × 2 pixels and 3 × 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two investigated beta diversity were strongly related to the development stage of a number of sampling plots in the tree species basal area method. To obtain beta diversity, the tree basal area method indicates better result than the tree species number method at representing similarity of regions which are located close together. In conclusion, NDVI is helpful for estimating the alpha diversity of trees over large areas when the vegetation is at the maximum growing season. Beta diversity could be obtained with the spectral heterogeneity of Landsat data. Future tree diversity studies using remote sensing data should select data sets when vegetation is at the maximum growing season. Also, forest tree diversity investigations can be identified by using higher-resolution remote sensing data such as ESA Sentinel 2 data which is freely available since June 2015.

We investigate Friedmann–Lamaitre–Robertson–Walker (FLRW) models with modified Chaplygin gas and cosmological constant, using dynamical system methods. We assume p = ( γ - 1 ) μ - A μ α as equation of state where μ is the matter-energy density, p is the pressure, α is a parameter which can take on values 0 < α ≤ 1 as well as A and γ are positive constants. We draw the state spaces and analyze the nature of the singularity at the beginning, as well as the fate of the universe in the far future. In particular, we address the question whether there is a solution which is stable for all the cases.

Background Soccer is a dynamic sport that involves high-intensity running, changes of direction, jumping and contact. Therefore, a proper warm-up duration is of great importance to optimize players'performance and minimize the risk of injury. Methods This study examined the responses of amateur young 16 players (age = 17.00 ± 0.81 years; height = 177.38 ± 5.50 cm; weight = 64.50 ± 5.45 kg) 25 min (min), 15 min and 8 min warm-up duration in 4 v 4 small-sided games (SSGs) with mini-goal formats. Participants are assessed using the Participant Classification Framework, they are categorized under Tier 2: Trained/Developmental. The SSG interventions were randomly assigned to three training intervention groups. The features of SSG are determined as size; 25 × 32 m, bout; 4 × 4 min, resting; 4 min. Before the SSG, same protocol was applied at different times in all warm-ups. Warm-up protocols consisted of 13 sections. The intervention time in each section decreased parallel to the total 25 min, 15 min and 8 min warm-up times. The rating of perceived exertion (RPE), heart rate (HR) responses, distance covered and technical activities were consistently recorded during all SSG sessions. A one-way repeated-measures ANOVA was used to assess significant differences in performance among the different warm-up duration. Results After the interventions, HR, total player load (TPL), successful passes (SP), unsuccessful passes (USP), interceptions and lost ball results demonstrated significant difference between the 25-min, 15-min and 8-min warm-up durations ( p < 0.05). Total distance, velocity, RPE and enjoyment results showed no significant difference between the 25-min, 15-min and 8-min warm-up duration ( p > 0.05). Results indicate that a 15-min warm-up duration provides an optimal balance between physiological and technical preparation, leading to improved HR responses, SP and interceptions compared to the 25-min and 8-min warm-ups. The 25-min warm-up decreased USP and lost ball occurrences compared to the 15-min and 8-min warm-ups. The 8-min warm-up resulted in a lower TPL, indicating reduced physiological demands. Conclusions The 15-min warm-up duration emerged as an optimal protocol, offering a time-efficient approach that enhances both technical performance and physiological readiness while avoiding unnecessary fatigue. This finding provides practical implications for coaches and practitioners in designing warm-up routines that maximize match readiness without overexertion.