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The frequency of neuropathy and predictive parameters in prediabetic cases from Turkiye
No full textIntroduction: Diabetic sensorimotor peripheral neuropathy causes patients to have foot injuries without realizing it. This condition may progress to diabetic foot ulcer; infections can include osteomyelitis and lower limb amputations. Managing diabetes and screening diabetic neuropathy is crucial to reducing patient mortality, quality of life, functionality, and the cost burden of complications to the healthcare system. We aim to contribute to the literature by comparing diagnostic methods and examining parameters that can predict neuropathy early. Material and methods: A total of 108 patients with a neuropathy score Douleur Neuropathique-4 (DN-4) above 4, 54 with known diabetes, and 54 with prediabetes were included. Fasting plasma glucose, oral glucose tolerance test, hemoglobin A1c (HbA1c), LDL-cholesterol, HDL-cholesterol, triglyceride, uric acid, vitamin B12, folic acid, creatinine, and complete urinalysis was performed on 108 patients included. Afterward, a monofilament test, tuning fork test, and electromyography were performed by the neurologist to prove neuropathy. Results: The frequency of neuropathy in the prediabetes group was found to be 0.40 f 0.49 % using EMG. This rate is 0.71 f 0.45 % for diabetic neuropathy. The difference is statistically significant (p = 0.001) in the pre-diabetic group, the neuropathy score (DN-4 score) was 5.1 f 0.9, the tuning fork test positivity was 0.18 f 0.39, and p = 0.001 was statistically significant compared to the diabetic group. Also, in the monofilament test, the rate of neuropathy in the prediabetes group was again statistically significant with 0.68 f 0.47 (p = 0.027). Total cholesterol (185.1 f 21.8, p = 0.003), high uric acid (5.11 f 1.27, p = 0.003), and low folic acid (4.5 f 1.05, p = 0.026) are found to be statistically significant between diabetic and prediabetic groups. Discussion and conclusion: In diagnosing neuropathy, monofilament, and diapason testing can be used in the clinical setting, and they have been found to be successful tests in the diagnosis of neuropathy. Also, our analysis indicates the relationship between low folic acid, high total cholesterol/uric acid levels, and prediabetic neuropathy. The role of intervening blood levels of those factors with medications in preventing neuropathy is unclear. We recommend further investigating all the patient's dietary habits to find possible risk factors, as well as investigating patients with low folic acid and high total cholesterol/ uric acid levels much more cautiously. Recommendation: Neuropathy should be screened in prediabetic and diabetic patients, and possible risk factors should be assessed periodically.for the timely completion of this study. The study protocol received institutional review board approval
The Impact of Antiretroviral Therapy on Electrocardiographic Parameters in Human Immundeficiency Virus-Positive Patients
No full textBackground Antiretroviral therapy (ART) has revolutionized the management of human immunodeficiency virus (HIV) infection by transforming it into a chronic but manageable condition. Despite its effectiveness in viral suppression and immune restoration, concerns remain regarding ART's potential impact on cardiovascular health, particularly on electrocardiographic (ECG) parameters. Objective This study investigated the effects of ART on ECG parameters in HIV-infected patients by analyzing pre- and post-therapy data. Methods A total of 83 HIV-positive patients were enrolled and evaluated for ECG parameters before and 3 months after ART initiation. Key parameters, including QRS duration, QT duration corrected by the Bazett formula (QTc interval), QRS-T angle, morphology in inferior leads, voltage in lead 1, and P-wave duration (MVP) score, were manually assessed. Statistical analyses compared pre- and post-ART values. Results No statistically significant changes were observed in ECG parameters post-ART. For example, QRS duration remained stable (pre-ART: 89.08 +/- 12.01 ms; post-ART: 88.94 +/- 10.00 ms, p = 0.849), as did QTc interval (pre-ART: 403.51 +/- 22.22 ms; post-ART: 404.84 +/- 14.91 ms, p = 0.563) and MVP ECG score (pre-ART: 3.02 +/- 0.95; post-ART: 2.98 +/- 0.87, p = 0.882). The QRS-T angle also showed no significant difference (p = 0.675). Conclusion ART does not appear to significantly affect ECG parameters in HIV-infected patients, supporting its favorable cardiac safety profile. These findings highlight the importance of regular ECG monitoring to ensure cardiovascular safety in patients undergoing ART
Predictors of Upper Extremity Function in Breast Cancer Survivors
No full textBackground and Purpose Deteriorated upper extremity function is a leading cause of potential disability and functional decline in breast cancer survivors (BCS) not only during their active treatment but also in their survivorship continuum. Fatigue and decreased muscle strength are the prominent ones that can account for decreased upper extremity function. Therefore, this study aimed to explore the potential contributors to the upper extremity function of BCS. Methods A total of 100 BCS were included. Handgrip strength (HGS) was assessed with a dynamometer while fatigue and self-reported upper extremity function were assessed with the Fatigue Severity Scale (FSS), Disabilities of Arm, Shoulder, and Hand Questionnaire (DASH), respectively. To control the potential confounding effect of the handedness, the mean value of HGS was used in the regression model. Results HGS was found to be significantly higher on the right side compared to the left side (22.71 +/- 3.51 kg vs. 21.45 +/- 3.66 kg, t = 5.328, p < 0.001). The mean scores of DASH and FSS were found 21.97 +/- 15.62, and 39.62 +/- 12.16, respectively. The multivariate linear regression analysis showed that a total of 26.9% of the cumulative variance in DASH is explained by the mean HGS, FSS, and body mass index (BMI) (F (4,95) = 11.793, p < 0.001, R-2 = 0.269, Cohen's f(2):0.54). There were no significant effects and/or interactions of any clinical features of BCS on DASH. FSS and DASH also significantly correlated with each other (r = 0.49, p < 0.001). Discussion This study showed that HGS shows itself as a significant outcome measure to predict upper extremity function in BCS. Because BCS can experience fatigue even years after the completion of primary treatment, it is noteworthy to consider it when evaluating the function of BCS
Analysis of Logistics 4.0 Service Provider Alternatives with CRITIC-Based WASPAS Method
No full textPurpose: This study aims to evaluate and identify the most suitable logistics service providers within the framework of Logistics 4.0, shaped by digital transformation and Industry 4.0 technologies. Logistics 4.0 seeks to optimize logistics processes using innovative technologies such as smart systems and big data analytics. In this context, selecting the right service provider is of strategic importance for businesses. This study intends to assist companies in making accurate decisions in this complex process. Method: The CRITIC (Criteria Importance Through Intercriteria Correlation) based WASPAS (Weighted Aggregated Sum Product Assessment) method was employed. The CRITIC method was used to determine the objective weights of the criteria, while the WASPAS method utilized these weights to calculate the overall performance scores of the alternatives. Findings: The results of the study reveal the key criteria that businesses should consider when selecting Logistics 4.0 service providers and identifying the top-performing service providers. Originality: This study highlights the advantages and effectiveness of using the combined CRITIC and WASPAS methods in the selection of service providers in the logistics sector. Additionally, it contributes to the literature on the selection of Logistics 4.0 service providers
Psychometric Properties of the Turkish Version of Perceived Inventory of Technological Competency as Caring in Nursing
No full textAIM: This research is to analyze the psychometric feature of the Perceived Inventory of Technological Competency as Caring in Nursing in the Turkish version. METHOD: A methodological study type was employed. Five hundred one nurses participated in the research, carried out between November 2021 and February 2022. The measures included a sociodemographic data collection tool and the Perceived Inventory of Technological Competency as Caring in Nursing-Turkish. To determine the psychometric properties of the scale, the validity study included content and construct validity analyses, and the reliability study was conducted using item analysis and split-half and Cronbach's alpha coefficients RESULTS: The Turkish adaptation of the Perceived Inventory of Technological Competency as Caring in Nursing comprised 18 questions collected under four sub-factors, explaining 56.08% of the total variance. According to the confirmatory factor analyses results, fit values were determined as comparative fit index = 0.94, normed fit index = 0.91, Trucker-lewis index = 0.92, goodness of fit index = 0.90, root mean square error of approximation = 0.070, incremental fit index = 0.94, chi(2) = 439.052, df = 126, p 0.70, respectively. CONCLUSION: The Turkish form of the Perceived Inventory of Technological Competency as Caring in Nursing showed adequate psychometric properties. The essential contribution of this study was that it provided a reliable and valid inventory to evaluate nurses' perceived technological competence as caring for the Turkish version
Machine learning models for predicting tibial intramedullary nail length
No full textBackgroundTibial intramedullary nailing (IMN) represents a standard treatment for fractures of the tibial shaft. Nevertheless, accurately predicting the appropriate nail length prior to surgery remains a challenging endeavour. Conventional techniques frequently depend on data obtained intraoperatively, which may prolong surgical time and elevate radiation exposure. This study employs anthropometric measurements to evaluate and contrast the efficacy of machine learning (ML) models in predicting tibial IMN length.MethodsA retrospective analysis was conducted on 163 patients who had undergone tibial IMN. Anthropometric data were collected, including the subject's height, shoe size, olecranon-to-5th metacarpal distance (OM), and tibial tuberosity-to-medial malleolus distance (TTMM). Four ML models, namely linear regression, random forest, decision tree, and XGBoost, were employed for the purpose of predicting tibial IMN length. The performance of the models was evaluated using the mean squared error (MSE) and the R-squared values.ResultsThe linear regression model demonstrated superior performance compared to the random forest, decision tree, and XGBoost models, with an R-squared value of 0.89, an MSE of 117.53, and a root mean squared error (RMSE) of 10.84 mm. The strongest correlation with IMN length was demonstrated by TTMM (r = 0.911), followed by height (r = 0.899) and OM (r = 0.811). Furthermore, TTMM provided the greatest contribution to prediction accuracy, thereby supporting its use as a reliable predictor in clinical settings. The correlation between shoe size and the dependent variable was weaker (r = 0.823), and the inclusion of shoe size in the model negatively impacted the prediction accuracy. Despite their ability to handle non-linear relationships, the random forest and XGBoost models yielded higher MSE values, indicating limited improvement over linear regression. These findings underscore the linear nature of the relationship between anthropometric variables and IMN length, with linear regression offering the most reliable predictions.ConclusionCombining anthropometric measurements with ML models, particularly linear regression, effectively predicts IMN length. This approach can streamline preoperative planning by reducing intraoperative measurements and minimizing surgery time and radiation exposure. Further validation with larger datasets is necessary to confirm these findings across diverse populations
A New Era in Diabetes Management: Generative Artificial Intelligence
No full textDiabetes mellitus (DM) is a rapidly increasing global health issue that requires effective selfmanagement to prevent complications and improve quality of life. In recent years, advancements in generative artificial intelligence (GenAI) have created new opportunities to support DM selfmanagement by providing personalized care solutions. This study is designed as a systematic review. Numerous studies in the literature have examined the contributions of GenAI models to DM self-management, and reviewing these studies is essential to provide a general framework on this topic. The primary aim of this study is to systematically examine research that utilizes GenAI in DM management. This systematic review was conducted in accordance with PRISMA guidelines. A comprehensive literature search was carried out between February and October 2024 across PubMed, Scopus, Web of Science, Google Scholar, Ulakbim, Türk Medline, and national databases. Using the keywords "diabetes," "generative artificial intelligence," and "diabetes self-management," studies published between 2018 and 2024 were identified. A total of 19 studies that met the inclusion criteria were analyzed in terms of the GenAI models used, application areas, and reported outcomes. Among the reviewed studies, GPT-based models were predominant, appearing in 53% of the research. In addition, models such as GAN, LSTM, WaveNet, GRU, Markov-Bayes, Google Bard, and Mobiguide were also utilized. Moreover, the findings of this study highlight that GenAI-based systems are widely adopted in DM selfmanagement and possess significant potential to facilitate this process. These systems not only provide information but also incorporate advanced support mechanisms that enhance patient monitoring and clinical decision-making processes. GenAI has made notable contributions to DM care, particularly by developing personalized care plans, offering tailored dietary and exercise recommendations, generating educational materials, predicting blood glucose (BG) levels, providing individualized guidance, and supporting clinical workflows. As GenAI continues to evolve and adapt to the specific contexts and demands of the medical field, its role in DM care is expected to become increasingly prominent. However, several challenges have been reported, including concerns over data security, privacy, misinformation generation, and suboptimal performance in detecting critical conditions such as hypoglycemia. Addressing these ethical, technical, and security-related limitations requires further research and technological advancements. Future studies should prioritize enhancing the reliability, usability, and diagnostic accuracy of GenAI applications to ensure their seamless integration into clinical practice
Classification issue of sustainability expenses within the scope of accounting standards and a case study
No full textGelişmekte olan ülkeler, ekonomik ve çevresel sorunlarla başa çıkabilmek amacıyla çeşitli düzenlemeler ve yaptırımlar uygulamaktadır. Bu bağlamda, işletmelerin sürdürülebilirlik stratejilerini etkin bir şekilde oluşturabilmesi için kaynakları verimli kullanmaları büyük önem taşımaktadır. Özellikle sermaye yatırımları, üretim verimliliği ve tasarruflar açısından kritik bir rol oynamaktadır. Ancak sürdürülebilirlik yatırımlarının finansal raporlama süreçlerinde doğru sınıflandırılmaması, muhasebe yöntemlerinin uygulanmasında zorluklarla karşılaşılabilmektedir. İşletmeler, çevresel ve ekonomik hedeflerine ulaşabilmek için yaptıkları yatırımların doğru şekilde muhasebeleştirilmemesi durumunda, bu durum raporlama süreçlerini ve yatırımcı güvenini olumsuz etkileyebilmektedir. Ayrıca, söz konusu sürdürülebilirlik harcamalarının finansal tablolarda tek bir kalemde birikmesi, kara para aklama gibi ciddi yasal ve etik sorunlara yol açabilir. Bu risklerin önlenebilmesi için, sürdürülebilirlik yatırımlarının finansal raporlamada doğru bir şekilde ayrıştırılması ve şeffaflığın artırılması gerektiği açıktır. Bu çalışma, sürdürülebilirlik harcamalarının muhasebe standartları çerçevesinde doğru şekilde sınıflandırılmasıyla ilgili karşılaşılan sorunları ele almakta ve Türkiye Sürdürülebilirlik Raporlama Standartları (TSRS) ile uyumlu bir uygulama örneği sunmaktadır. 2020-2024 yılları arasında organize sanayi bölgesinde faaliyet gösteren yerel bir işletmenin entegre raporları ve konsolide finansal raporları incelenmiştir. Çalışma, bu süreçlerde karşılaşılan zorlukları ve sürdürülebilirlik yatırımlarının muhasebeleştirilmesinin finansal raporlama süreçlerine entegrasyonunun nasıl gerçekleştirildiğini ortaya koymaktadır. Sürdürülebilirlik harcamalarının doğru biçimde sınıflandırılması, yalnızca finansal raporlama süreçlerinin doğruluğunu artırmakla kalmayıp, işletmelerin çevresel ve ekonomik sorumluluklarını yerine getirirken daha sağlıklı performans değerlendirmeleri yapmalarına da olanak tanımaktadır. Bu sınıflandırma, aynı zamanda kurumsal itibarın güçlenmesine ve paydaş güveninin artmasına katkı sağlamaktadır. Ayrıca, işletmelerin sürdürülebilirlik risk ve fırsatlarına dair bilgilerini şeffaf bir biçimde raporlamaları, karşılaştıkları zorlukların etkin şekilde yönetilmesini sağlayacaktır.Developing countries are implementing various regulations and sanctions to address economic and environmental challenges. In this context, it is crucial for businesses to efficiently utilize resources in order to effectively create sustainability strategies. Capital investments, particularly in terms of production efficiency and savings, play a critical role. However, the failure to properly classify sustainability investments in financial reporting processes brings about challenges in accounting practices. When businesses fail to properly account for investments aimed at achieving environmental and economic goals, this can negatively impact reporting processes and investor confidence. This study addresses the issues encountered in the proper classification of sustainability expenditures within the framework of accounting standards and presents a case study aligned with the Turkey Sustainability Reporting Standards (TSRS). The integrated reports and consolidated financial statements of local businesses operating in organized industrial zones between 2020 and 2023 were examined. The study highlights the challenges encountered in these processes and demonstrates how the integration of sustainability investments into financial reporting processes is carried out.Proper classification of sustainability expenditures not only enhances the accuracy of financial reporting processes but also enables businesses to make more reliable performance assessments while fulfilling their environmental and economic responsibilities. This classification also contributes to the strengthening of corporate reputation and the enhancement of stakeholder trust. Furthermore, the transparent reporting of sustainability risks and opportunities will allow businesses to manage the challenges they face more effectively
Investigating the Effectiveness of Pelvic Floor Muscle Training, Including Sensor-Based Diaphragm Exercises in Women With Stress Urinary Incontinence: A Randomized Controlled Study
No full textObjective: To compare the effects of pelvic floor muscle exercises (PFME) combined with standard diaphragm exercises and 360° expanded diaphragm exercises on urinary symptoms, pelvic floor muscle (PFM) function, and respiratory function in women with stress urinary incontinence (SUI). Design: Randomized controlled study. Setting: The study conducted between November 2023 and 2024. Participants: Women with SUI (n=74). Interventions: Participants were randomly allocated into 2 groups: (1) PFME + standard diaphragm (n=37) and (2) PFME + 360° expanded diaphragm exercises (n=37). The 360° exercises were taught using 2 sensor-based biofeedback devices. Both groups completed an 8-week program with weekly sessions. Main Outcome Measures: The primary outcome was precontraction of the PFM. Secondary outcomes included the Incontinence Severity Index, The International Consultation on Incontinence Questionnaire-Short Form, PFM, and respiratory functions [maximum inspiratory pressure (MIP) and maximum expiratory pressure (MEP)]. Results: The sociodemographic and clinical characteristics of the PFME + standard diaphragm (49.29±6.73y) and the PFME + 360° expanded diaphragm exercises groups (50.97±7.70y) were similar (P>.05). Before and after the 8-week exercise program, both groups showed significant improvement in PFM functions as well as in incontinence severity index, incontinence questionnaire-short form, and MIP and MEP values (P<.05). Additionally, the initiation time for PFM contraction during the Valsalva maneuver (precontraction of PFM) was reduced in the PFME + 360° expanded diaphragm exercises group after treatment (P=.010). Conclusions: This study demonstrated that PFME combined with various diaphragm exercises improved urinary symptoms and PFM function in women with SUI. Specifically, PFME with 360° expansion diaphragm exercises reduced the initiation time of PFM contraction during Valsalva. This approach may enhance PFME effectiveness in women with impaired precontraction ability. As this study focused only on women, future research should explore the efficacy of similar interventions in sex-diverse populations. © 2025 Elsevier B.V., All rights reserved.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITAK, (123S174); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITA
A Scale Development Study: Ethical Sensitivity Towards Artificial Intelligence and Robot Nurses
No full textBackground Socially assistive robots use social interactions to monitor, coach, provide companionship, and support health-promoting activities. However, the widespread use of artificial intelligence and robot nurse applications in many areas leads to ethical dilemmas and concerns.Methods A methodological study was conducted in two phases: (1) development of the scale through a literature review and interviews related to Ethical Sensitivity towards Artificial Intelligence and Robot Nurses; (2) confirming construct validity, criterion-related validity and reliability of the developed scale. The data were collected from 356 nursing students studying at the Nursing Department of a university in Turkey between November 2022 and December 2022.Results The scale consists of 17 items and four sub-dimensions, which accounts for 55.84% of the total variance. The Cronbach's alpha value of the scale was 0.83, which was considered as significant. The Kaiser-Meyer-Olkin value of the Ethical Sensitivity Scale for Artificial Intelligence and Robot Nurses was 0.76, and its Bartlett's Test of Sphericity results were as follows: chi 2 = 1174.25, p = 0.000. According to the results of the Confirmatory Factor Analysis, fit indices were determined as follows: chi 2/SD = 1.979, Root Mean Square Error of Approximation = 0.074, Comparative Fit Index = 0.894, Incremental Fit Index = 0.897 and Goodness of Fit Index = 0.880.Conclusion The Ethical Sensitivity Scale for Artificial Intelligence and Robot Nurses was determined as a valid and reliable measurement tool. It is recommended that the Ethical Sensitivity Scale for Artificial Intelligence and Robot Nurses should be used in different sample groups, different cultures and societies