This study investigated whether baseline serum level of C-reactive protein (CRP)/albumin ratio is associated with infarct localization, number of vascular lesions, and in-hospital mortality in patients undergoing primary percutaneous coronary intervention (PCI) for acute ST elevation myocardial infarction (STEMI). The study population consisted of 116 patients diagnosed with STEMI. The CRP/albumin ratio at first admission, cardiac troponin-I (cTnI), PCI results, and clinical outcomes were recorded. The mean CRP/albumin ratio, cTnI level, and mean number of vascular lesions were significantly higher in non-survivors than in survivors (p = 0.006, p = 0.004, and p = 0.007, respectively). Multivariate logistic regression analysis demonstrated that the CRP/albumin ratio and number of coronary artery lesions were independent predictors of mortality in STEMI patients. According to these analyses, the presence of ? 2 vessel lesions was the most important predictor of mortality, with an odds ratio of 2.009 (95% confidence interval: 1.191?3.387, p = 0.009). This study demonstrates the potential utility of the CRP/albumin ratio for predicting the clinical outcome of patients with STEMI. In addition, the presence of ? 2 vascular lesions contributed to a 2-fold increase in mortality rate in STEMI patients.

This study widens earlier research, which examined the relationship between financial performance, sustainability reporting (SR) and integrated reporting (IR). High performance enterprises and control group companies were compared with regard to their state of readiness to IR and SR. The population includes non-financial companies in Borsa Istanbul (BIST). Our hypothesis is that HPC will exceed Non-HPC in IR and SR. We evaluate two groups disclosure practices based on an IR and sustainability reported matrix developed from the International Integrated Reporting Committee (IIRC) and Global Reporting Initiative (GRI) standards. The findings reveal that HPCs in Turkey show more on the disclosures of SR and IR compared to Non-HPCs. The paper provides evidence that support the suggestion that Turkish HPCs disclose better information compared to Non-HPCs and achieved to support conclusions of previous research.

Context: Extreme rates of vascular and all-cause mortality, especially in the clopidogrel arm of the PLATO (PLATelet Inhibition and Clinical Outcomes) trial, raise concerns of data accuracy and call for independent verification of vital records in the national death registries. Two recently completed acute coronary syndrome (ACS) trials, TRACER and ATLAS ACS 2 (Thrombin Receptor Antagonist for Clinical Event Reduction in ACS and Anti-Xa Therapy to Lower Cardiovascular Events in Addition to Standard Therapy in Subjects with ACS-Thrombolysis in Myocardial Infarction), provide a valuable opportunity to match mortality numbers among 3 similar studies. Objective: To compare the rates of vascular and all-cause mortality in the PLATO, TRACER and ATLAS ACS 2 trials. Results: Despite a shorter mean follow-up (277 days) for the PLATO trial than for the TRACER (502 days) or ATLAS ACS 2 (393 days) trials, both vascular (5.1%) and all-cause (5.9%) mortality in PLATO were higher than in the TRACER (3.2 and 4.9%) or ATLAS ACS 2 (4.1 and 4.5%) control arms, respectively. Adjusting for follow-up duration, rates of vascular (0.0184/day), or all-cause (0.0213/day) mortality in PLATO differ importantly from daily death rates in TRACER (0.0063 and 0.0097) and ATLAS ACS 2 (0.0104 and 0.0115), suggesting that the risk of death in the control PLATO arm was approximately double that of the other trials. The mismatch is particularly striking considering that ATLAS ACS 2 enrolled more STEMI (ST-segment elevation in myocardial infarction) patients (50.9%) than PLATO (38.0%). Conclusions: Both overall and follow-up duration-adjusted mortality rates in PLATO far exceeded the risk of death observed in the two recent ACS trials. The background STEMI rates are not likely to be responsible for the PLATO mortality paradox. These data provide an additional reason to request an independent audit of the deceased PLATO clopidogrel cohort.

Bu çalışmanın amacı farklı sektörlerde faaliyet gösteren lider rakip firmaların sosyal medya etkinliklerinin ölçülmesidir. Bu kapsamda, 2018 Şubat ayı boyunca Twitter kullanıcılarının kozmetik, pazaryeri ve elektronik sektöründe faaliyet gösteren rakip firmalar hakkında yaptıkları paylaşımlar ve bu firmaların kurumsal Twitter hesaplarından yapmış oldukları paylaşımlar Sosyal Medya Madenciliği yöntemi ile analiz edilmiştir. Firmalar hakkındaki tweet sayısı, tweet değeri (olumlu, olumsuz, nötr), takipçi kazanımı, yanıt sayısı, retweet sayısı ve beğeni sayısı başlıklarından oluşan Twitter etkinliğinin ölçüm kriterleri ele alınarak bir başarı sıralaması yapılmış ve en başarılı firmanın kozmetik, en düşük sıralamaya sahip firmanın ise elektronik sektöründen olduğu tespit edilmiştir. Tweet değerini saptayabilmek için Duygu Analizi gerçekleştirilmiştir ve olumlu tweet oranının kozmetik firmaları için daha fazla olduğu sonucuna ulaşılmıştır.

This study presents a modeling framework that formulates goal prediction as a binary classification problem (1 for a goal, 0 for no goal), based on shot records from the Turkish Super League between the 2020–2025 seasons. In this study, Naive Bayes (NB) and Support Vector Machine (SVM), which are supervised machine learning algorithms, used for prediction models. The focus of this study is to build a model and improve its overall performance by addressing the significant class imbalance between goals and non-goals in the original dataset. The results of machine learning models created for a balanced dataset were compared, and the findings showed that SVM models were relatively more successful in adapting to the complex data structure of football. Feature importance analyses confirmed that distance is the most dominant predictor of shot success. It has appeared that the most important variables are related to distance and angle. This study contributes to the literature with its predictive power and model transparency, and offers a decision support framework, especially for the Turkish Super League.

This study examines the risk level of the logistics sector, which is a key sub-component of the service sector in Türkiye, by comparing it with other service sub-sectors that have a high share within the sector, namely retail trade, electricity, gas, and steam, and telecommunications indices. Daily data from the period between March 2, 2020, and October 8, 2025, when volatility in financial markets increased significantly after COVID-19, was used for the analysis. First, returns for the indices were generated, and various non-linearity tests were used to determine whether these returns were non-linear. Next, the stationarity properties of the series were tested using non-linear unit root tests, and the appropriate ARMA(p,q) model was determined for each series based on information criteria. To examine the volatility structure of the return series, symmetric conditional heteroskedasticity models (GARCH, GARCH-M) and asymmetric effect models (T-GARCH, E-GARCH, and GJR-GARCH) were applied. The most suitable model was selected by considering error criteria and log-likelihood values. The findings reveal that the risk levels among the service sector indices are close to each other but show sectoral differentiation. According to the analysis, the telecommunications sector had the highest risk during the period under review, followed closely by the logistics sector. In contrast, the retail trade sector was identified as the sub-sector with the lowest volatility.