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    Bilateral external oblique intercostal plane block (EOIPB) in patients undergoing laparoscopic cholecystectomy
    (Saudi Med J, 2023) Korkusuz, Muhammet; Basaran, Betul; Et, Tayfun; Bilge, Aysegul; Yarimoglu, Rafet; Yildirim, Hasan
    Objectives: To measure tramadol intake in the first 24 hours post-surgery. In addition, pain scores and quality of recovery were evaluated as secondary outcomes.Methods: A total of 80 adult patients scheduled for laparoscopic cholecystectomy were randomized into 2 groups (with and without external oblique intercostal plane block [EOIPB]). Control group of patients received standard multimodal analgesia, EOIPB was applied on each side to patients in EOIPB group in addition to multimodal analgesia. The primary outcome was to evaluate tramadol consumption at postoperative 24 hours. Secondary outcomes were evaluating the Numeric Rating Scale (NRS) scores, postoperative Quality of Recovery score (QoR-15), sedation score, the incidence of nausea and vomiting, and antiemetic consumption.Results: In EOIPB group, median (Q1, Q3) tramadol consumption values for 24 hours (0 [0,50] mg) were found to be significantly lower than the control group (50 [50,100] mg) (median difference-50) (p<0.001). NRS values during rest and motion were lower in EOIPB group compared to the control group at all measurement points within 24 hours (p<0.05). The total QoR-15 scores were significantly higher in EOIPB group compared to the control group (p<0.001). No differences were detected in other secondary outcome parameters. Conclusion: External oblique intercostal plane block resulted in less postoperative tramadol consumption. However, there were no minimal clinically important differences about postoperative opioid consumption. On the other hand, compared to multimodal analgesia addition of EOIPB improved overall QoR-15 scores at postoperative 24 hours.
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    Descriptor-Based Explainable QSAR Modeling Approaches for the Prediction of Respiratory Toxicity of Volatile and Gas-Phase Chemicals
    (John Wiley and Sons Ltd, 2025) Karaduman, Gul; Yildirim, Hasan
    Toxic gases pose significant risks to human health and ecosystems because of their capacity to deplete oxygen levels and disrupt critical physiological functions. Effective management of these hazards is crucial, given their widespread use in various industrial sectors, including agriculture, metallurgy, pharmaceuticals, and plastics. A dataset of 229 gaseous-phase chemicals from authoritative toxicological sources was used to develop robust binary quantitative structure–activity relationship (QSAR) models for predicting toxicity. Employing molecular descriptor selection, correlation analyses, and outlier detection, random forest and XGBoost emerged as the most accurate models, each achieving a high accuracy of 0.915, with ROC areas of 0.992 and 0.986, respectively. SHapley Additive exPlanations (SHAP) were applied to enhance the explainability of the best model, providing global and local insights into the importance and direction of each descriptor. Key features influencing toxicity were identified globally, while locally, descriptors were ranked on a molecule-by-molecule basis, offering guidance for mitigating toxic effects through structural modifications. Although numerous studies have focused on predicting the toxicity of chemical gases, few have systematically analyzed the specific molecular descriptors driving toxicity. This study addresses this gap by providing a descriptor-based analysis, offering deeper mechanistic insights, and enabling more accurate predictions. The QSAR approach aligns with the 4R principles, promoting ethical, cost-effective, and efficient alternatives to animal testing, thus contributing to safer chemical design and environmental monitoring. © 2025 John Wiley & Sons Ltd.
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    An investigation of machine learning algorithms for prediction of lumbar disc herniation
    (Springer Heidelberg, 2023) Kocaman, Hikmet; Yildirim, Hasan; Goeksen, Aysenur; Arman, Gokce Merve
    The prevalence of lumbar disc herniation (LDH), which makes patients' daily activities more difficult and reduces their quality of life, has tended to increase recently. Many risk factors associated with LDH have been reported. In this study, LDH was predicted using machine learning techniques using measures of the lumbar paraspinal muscles, lumbar vessels cross-sectional area (CSA), and lumbar sagittal curve. Three hundred and forty-four individuals' MR scans were prospectively enrolled (264 with LDH and 80 healthy). Predictive factors were the lumbar sagittal curve and the cross-sectional areas of the lumbar paraspinal muscles and vessels from sagittal and axial MR images. The measurements have been analyzed via ten different and most common machine learning algorithms by considering a comprehensive parameter tuning and cross-validation process. The variable importance results have been also presented. XGBoost algorithm among all algorithms has provided the best results in terms of different classification metrics including f-score ( 0.830 ), AUC ( 0.939 ), accuracy ( 0.922 ), and kappa ( 0.779 ). The findings of this study demonstrated that cross-sectional areas of the quadratus lumborum and abdominal aorta can be utilized as a reliable indicator of LDH. Consequently, the developed model and the variables found to be important may guide to healthcare professionals to make more accurate and effective decisions in terms of prediction the LDH.
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    A Novel Regularized Extreme Learning Machine Based on L1-Norm and L2-Norm: a Sparsity Solution Alternative to Lasso and Elastic Net
    (Springer, 2023) Yildirim, Hasan; Ozkale, M. Revan
    The aim of this study is to present a new regularized extreme learning machine (ELM) algorithm that can perform variable selection based on the simultaneous use of both ridge and Liu regressions in order to cope with some disadvantages of ELM and its variants such as instability and poor generalization performance and lack of sparsity. The proposed algorithm was compared with the classical ELM as well as the variants based on ridge, Liu, Lasso and Elastic Net approaches by cross-validation process and best tuning parameter over seven different real-world applications and their performances were presented comparatively. The proposed algorithm outperformed ridge, Lasso and Elastic Net algorithms in training performance prediction (average 40%) and stability (average 80%) and in test performance prediction (average 20%) and stability (60%) in the majority of the data. In addition, the proposed ELM was found to be more compact (better sparsity capability) with lower norm values. The results confirmed that the proposed ELM presents more stable and sparse solutions with better generalization performance than any other algorithm under favorable conditions. The findings based on experimental study via real-world applications indicate that the proposed ELM provides effective solutions to the mentioned drawbacks and yields more stable and sparse performance with better generalization capability than its competitors. Consequently, the proposed algorithm represents a powerful alternative both regression and classification tasks in machine learning field due to its theoretical flexibility.
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    Predictors of the masticatory muscle activity during chewing in patients with myogenous temporomandibular disorder
    (Springer Heidelberg, 2023) Yildiz, Nazim Tolgahan; Kocaman, Hikmet; Yildirim, Hasan
    ObjectivesThe aim of the study was to identify predictors of the masticatory muscle activity during chewing (MMA) of the masseter and temporalis anterior (TA) muscles in patients with unilateral myogenous temporomandibular disorder (mTMD).Materials and methodsThis observational and cross-sectional study included 109 patients diagnosed with unilateral mTMD. Surface electromyography was used to separately evaluate the MMA of the masseter and TA on the affected and unaffected sides. Also, pain intensity (with a visual analog scale), pressure pain threshold (with an algometer), active pain-free maximum mouth opening and temporomandibular joint lateral movements (with a ruler), cervical range of motions (with a goniometer), and TMD severity (with a Fonseca Anamnestic Index) were assessed. Various statistical methods were used to predict the MMA of the masseter and TA, including standard, forward, and best subsets multiple regression models.ResultsWhile there were significant correlations between the MMA of the masseter and TA and pain intensity, pressure pain threshold values, and TMD severity, they were not found with other variables. These parameters were also predictive factors for MMA of both muscles (p < 0.05).ConclusionsAccording to the present study, pain intensity, muscle and joint tenderness, and the severity of the disorder are predictive factors for MMA of the masseter and TA muscles in patients with mTMD. It is recommended that these parameters be considered when establishing clinical evaluation and treatment programs focusing on MMA in patients with mTMD.Clinical relevanceThe pain intensity, masticatory muscles and TMJ tenderness, and disorder severity are predictors for MMA of the masseter and TA in patients with mTMD. Pain intensity has the most significant importance.
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    The Reliability and Validity of the Modified Four Square Step Test in Individuals with Anterior Cruciate Ligament Reconstruction
    (Springer Heidelberg, 2023) Kocaman, Hikmet; Canli, Mehmet; Alkan, Halil; Yildirim, Hasan; Yildiz, Nazim Tolgahan
    BackgroundThe modified four square step test (mFSST) is frequently used in the evaluation of dynamic balance in individuals with balance problems. However, the reliability of the mFSST has not been examined in individuals undergoing anterior cruciate ligament reconstruction (ACLR) surgery.PurposeThe aim of this study was to examine the test-retest reliability of the mFSST in individuals undergoing ACLR surgery.MethodsForty-eight patients who had ACLR surgery were included in this study. Patients performed a total of four mFSSTs, two times each, by two different raters over seven days.ResultsIn the current study, the mFSST demonstrated excellent test-retest and inter-rater reliability. The intraclass correlation coefficient for mFSST was 0.92. The standard error of measurement and minimal detectable change for mFSST were 0.15 and 0.41, respectively.ConclusionThe mFSST has excellent test-retest and inter-rater reliability in patients with ACLR. It is a valid and reliable tool for evaluating dynamic balance in patients with ACLR. We think that mFSST, which is a clinical evaluation test, can be preferred because it is easy to score and does not require special equipment.