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Acute effect of hydrogen-rich water on physical, perceptual and cardiac responses during aerobic and anaerobic exercises: a randomized, placebo-controlled, double-blinded cross-over trial
(Frontiers Media Sa, 2023) Jebabli, Nidhal; Ouerghi, Nejmeddine; Abassi, Wissal; Yagin, Fatma Hilal; Khlifi, Mariem; Boujabli, Manar; Bouassida, Anissa
Molecular hydrogen (H2 gas) dissolved in water to produce Hydrogen-Rich Water. Hydrogen-Rich Water (HRW) is considered as ergogenic aid in different exercise modes. However, acute pre-exercise HRW ingestion effect is unclear regarding athlete performance. This study aimed at investigating acute effect of HRW ingestion on aerobic and anaerobic exercise performance. Twenty-two male amateur middle-distance runners volunteered to participate in this study. In a randomized, double-blind study design, all players ingested 500 mL of HRW or placebo (PLA) supplement 30 min before the start of the tests. Over 4 days, maximal aerobic speed of Vameval test (MAS), time to exhaustion at MAS (Tlim), squat jump (SJ), counter-movement jump (CMJ) and five jump test (5JT) were evaluated. Also, rate of perceived exertion (RPE) and peak heart rate (HRpeak) were measured during the aerobic tests. For Vameval test, HRW ingestion improved MAS, HRpeak and RPE compared with the placebo condition. For Tlim test, HRW ingestion demonstrated improvements in time to exhaustion, RPE and HRpeak. However, no significant change was observed between HW and placebo conditions in SJ, CMJ, 5JT. 500 mL of HRW can significantly improve HRpeak, time to exhaustion, RPE, with no significant effect on MAS, jumping performance in amateur endurance athletes.
Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study
(Frontiers Media Sa, 2024) Yilmaz, Rustem; Yagin, Fatma Hilal; Colak, Cemil; Toprak, Kenan; Samee, Nagwan Abdel; Mahmoud, Noha F.; Alshahrani, Amnah Ali
Introduction Acute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF. Methods In this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients' demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model's efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model's incorporated risk factors. Results White blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF. Conclusion The results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.
Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study (vol 11, 1285067, 2024)
(Frontiers Media Sa, 2024) Yilmaz, Rustem; Yagin, Fatma Hilal; Colak, Cemil; Toprak, Kenan; Samee, Nagwan Abdel; Mahmoud, Noha F.; Alshahrani, Amnah Ali
[Abstract Not Available]
Are there differences between Mediterranean diet and the consumption of harmful substances on quality of life?-an explanatory model in secondary education regarding gender
(Frontiers Media Sa, 2023) Melguizo-Ibanez, Eduardo; Zurita-Ortega, Felix; Ubago-Jimenez, Jose Luis; Badicu, Georgian; Yagin, Fatma Hilal; Gonzalez-Valero, Gabriel; Ardigo, Luca Paolo
BackgroundAdolescence is a key life stage in human development. It is during this stage of development that healthy and physical behaviors are acquired that will last into adulthood. Gender differences in the acquisition of these behaviors have been observed. This research aims to (a) study the levels of Mediterranean diet adherence, quality of life and alcohol and tobacco consumption as regarding the gender of the participants and (b) study the effects of the variable adherence to the Mediterranean diet, alcohol consumption and tobacco consumption on quality of life as a function of the gender of the participants.MethodsA non-experimental, cross-sectional, exploratory study was carried out in a sample of 1,057 Spanish adolescents (Average Age = 14.19; Standard Deviation = 2.87).ResultsThe comparative analysis shows that the male teenagers shows a higher Mediterranean diet adherence compared to the male adolescents (p <= 0.05) and a higher consumption of alcoholic beverages (p <= 0.05). On the contrary, adolescent girls show a higher consumption of alcoholic beverages than male participants (p <= 0.05). The exploratory analysis indicates that for boys, alcohol consumption has a beneficial effect on the quality of life of adolescents (beta = 0.904; p <= 0.001).ConclusionIn this case, participants show differences in the levels of Mediterranean diet adherence, consumption of harmful substances and quality of life according to gender. Likewise, there are different effects between the variables according to gender. Therefore, gender is a key factor to consider during adolescence.
ARM: An Interactive Web Software for Association Rules Mining and an Application in Medicine
(Ieee, 2019) Percin, Ibrahim; Yagin, Fatma Hilal; Guldogan, Emek; Yologlu, Saim
In this study, it is aimed to develop a user-friendly, interactive web software for association rules mining. In the developed software, among the association rule methods; Filtered Associatior, Apriori, Frequent Pattern Growth, Predictive Apriori, Generalized Sequential Patterns, HotSpot, Tertius algorithms are used. In addition, association rules algorithms have certain limitation(s) regarding the structure of the data set. Therefore, preprocess menu in the software includes missing value assignment and variable type conversion, methods. In order to evaluate the association rules, support and confidence criteria are present in the software. However, it is not always possible to distinguish interesting and important rules only according to criteria of support and confidence. Therefore, in the proposed software; leverage, lift and conviction criteria are also included. A medical application is performed by using association rules mining, and the experimental results are evaluated based on the outputs of the developed software.
Assessment of Hematological Predictors via Explainable Artificial Intelligence in the Prediction of Acute Myocardial Infarction
(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Yilmaz, Rustem; Yagin, Fatma Hilal; Raza, Ali; Colak, Cemil; Akinci, Tahir Cetin
Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach. The purpose of this research is to implement the principles of explainable artificial intelligence (XAI) in the analysis of hematological predictors for AMI. In this retrospective analysis, 477 (48.6%) patients with AMI and 504 (51.4%) healthy individuals were enrolled and assessed in predicting AMI. Of the patients with AMI, 182 (38%) had an ST-segment elevation MI (STEMI), and 295 (62%) had a non-ST-segment elevation MI (NSTEMI). Demographic and hematological information of the patients was analyzed to determine AMI. The XAI approach combined with machine learning approaches (Extreme Gradient Boosting, XGB; Adaptive Boosting, AB; Light Gradient Boosting Machine, LGBM) was applied for the estimation of AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). The SHAP approach was used to explain the predictions intuitively. After selecting the 10 most important hematological parameters for AMI, the LGBM model achieved 83% and 74% accuracy for prediction of AMI, and distinguishing subgroups of AMI (STEMI and NSTEMI), respectively. SHAP results showed that neutrophil (NEU), white blood cell (WBC), platelet width of distribution (PDW), and basophil (BA) were the most important for AMI prediction. Mean corpuscular volume (MCV), BA, monocytes (MO), and lymphocytes (LY) were the most important hematological parameters that distinguish STEMI from NSTEMI. The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up of patients with AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). Analyzing readily accessible hemogram parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals.
Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model
(Mdpi, 2024) Aygun, Umran; Yagin, Fatma Hilal; Yagin, Burak; Yasar, Seyma; Colak, Cemil; Ozkan, Ahmet Selim; Ardigo, Luca Paolo
This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms-Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)-were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868-0.929) and area under the ROC curve (AUC) of 0.940 (0.898-0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.
Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research
(Mdpi, 2023) Yagin, Burak; Yagin, Fatma Hilal; Colak, Cemil; Inceoglu, Feyza; Kadry, Seifedine; Kim, Jungeun
Aim: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients. Method: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the black box problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed. Results: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p <= 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p <= 0.05) genes were also determined to increase the risk of metastasis in BC. Conclusion: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.
Chronic Disease Management of Children Followed with Type 1 Diabetes Mellitus
(Galenos Publ House, 2023) Baysal, Senay Gueven; Ciftci, Nurdan; Duendar, Ismail; Bueyuekavci, Mehmet Akif; Yagin, Fatma Hilal; Camtosun, Emine; Dogan, Derya Guemues
Objective: With the diagnosis of chronic illness in children, a stressful period is likely to begin for both the affected child and their families. The aim of this study was to investigate the factors affecting chronic disease management by the parents of children diagnosed with type 1 diabetes mellitus (T1DM).Methods: The sample consisted of 110 children, aged between 4-17 years and their mothers. The patients had been diagnosed with T1DM for at least one year, and had attended pediatric endocrinology outpatients or were hospitalized in a single center. First, sociodemographic information about the child with T1DM were obtained. Then, the Family Management Measure (FaMM) was applied. The FaMM is constructed to measure family functioning and management in families who have a child with a chronic illness.Results: Paternal years of education (p=0.036), family income (p=0.008), insulin pump use (p=0.011), and time elapsed after diagnosis (p=0.048) positively affected both the management of T1DM and the child's daily life. However, presence of chronic diseases in addition to T1DM (p=0.004) negatively affected diabetes management. Higher maternal education year (p=0.013) and family income level (p=0.001) increased parental mutuality scores. However, as the time after diagnosis increased, parental mutuality scores decreased.Conclusion: It is important to evaluate the child with chronic disease with a biopsychosocial approach. This approach aims to evaluate the problems of the child and his/her family who experience the disease with a holistic approach.
Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease
(Frontiers Media Sa, 2023) Edache, Emmanuel Israel; Uzairu, Adamu; Mamza, Paul Andrew; Shallangwa, Gideon Adamu; Yagin, Fatma Hilal; Samee, Nagwan Abdel; Mahmoud, Noha F.
The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo & BOTTOM;2 = 0.5548, Q_lto & BOTTOM;2 = 0.5278, R & BOTTOM;2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo & BOTTOM;2 = 0.7033, Q_lto & BOTTOM;2 = 0.6827, Q_lmo & BOTTOM;2 = 0.6305, R & BOTTOM;2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.
Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction
(Mdpi, 2024) Arslan, Ahmet Kadir; Yagin, Fatma Hilal; Algarni, Abdulmohsen; AL-Hashem, Fahaid; Ardigo, Luca Paolo
Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model's predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.
A Computer-Assisted Diagnosis Tool for Classifying COVID-19 based on Chest X-Ray Images
(Duzce Univ, Fac Medicine, 2021) Yagin, Fatma Hilal; Guldogan, Emek; Ucuzal, Hasan; Colak, Cemil
Objective: Since COVID-19 is a worldwide pandemic, COVID-19 detection using a convolutional neural network (CNN) has been an extraordinary research technique. In the reported studies, many models that can predict COVID-19 based on deep learning methods using various medical images have been created; however, clinical decision support systems have been limited. The aim of this study is to develop a successful deep learning model based on X-ray images and a computer-assisted, fast, free and web-based diagnostic tool for accurate detection of COVID-19. Methods: In this study a 15-layer CNN model was used to detect COVID-19 using X-ray images, which outperformed many previously published CNN models in terms of classification. The model performance is evaluated according to Accuracy, Matthews Correlation Coefficient (MCC), F1 Score, Specificity, Sensitivity (Recall), Youden's Index, Precision (Positive Predictive Value: PPV), Negative Predictive Value (NPV), and Confusion Matrix (Classification matrix). In the second phase of the study, the computer-aided diagnostic tool for COVID-19 disease was developed using Python Flask library, JavaScript and Html codes. Results: The model to diagnose COVID-19 has an average accuracy of 98.68 % in the training set and 96.98 % in the testing set. Among the evaluation metrics, the minimum value is 93.4 % for MCC and Youden's index, and the maximum value is 97.8 for sensitivity and NPV. A higher sensitivity value means a lower false negative (FN) value, and a low FN value is an encouraging outcome for COVID-19 cases. This conclusion is crucial because minimizing the overlooked cases of COVID-19 (false negatives) is one of the main goals of this research. Conclusions: In this period when COVID-19 is spreading rapidly around the world, it is thought that the free and web-based COVID-19 X-Ray clinical decision support tool can be a very effective and fast diagnostic tool. The computer-aided system can assist physicians and radiologists in making clinical decisions about the disease, as well as provide support in diagnosis, follow-up, and prognosis. The developed computer-assisted diagnosis tool can be publicly accessed at http://biostatapps.inonu.edu.tr/CSYX/..
Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome
(Peerj Inc, 2024) Yagin, Fatma Hilal; Shateri, Ahmadreza; Nasiri, Hamid; Yagin, Burak; Colak, Cemil; Alghannam, Abdullah F.
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS. Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequency-matched groups, were used to develop the XAI-based model. The dataset contained 832 metabolites, and after feature selection, the model was developed using only 50 metabolites, meaning less medical knowledge is required, thus reducing diagnostic costs and improving prognostic time. The computational method was developed using six different ML algorithms before and after feature selection. The final classification model was explained using the XAI approach, SHAP. The best-performing classification model (XGBoost) achieved an area under the receiver operating characteristic curve (AUCROC) value of 98.85%. SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. Besides the robustness of the methodology used, the results showed that the combination of ML and XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS.
Developmental characteristics of Williams-Beuren syndrome and evaluation of adaptive behavioral skills
(Tubitak Scientific & Technological Research Council Turkey, 2023) Guven Baysal, Senay; Arslan, Feyzullah Necati; Buyukavci, Mehmet Akif; Yagin, Fatma Hilal; Ekici, Cemal; Esener, Zeynep; Gumus Dogan, Derya
Background/aim: Williams-Beuren syndrome (WBS) is a rare genetic disorder with delays in language and cognitive development, but, with increased awareness of clinical features and a reliable diagnostic test, WBS is becoming more widely recognized in childhood. Adaptive behavior skills and/or maladaptive behavior are important for the prognosis of individuals with WBS. The aim of this study was to investigate the clinical and developmental characteristics of patients with WBS and further increase awareness about it by evaluating the adaptive skills and maladaptive behaviors of the patients.Materials and methods: The data of WBS patients followed-up at the Developmental Behavioral Pediatrics Unit were reviewed. Patient data on perinatal and postnatal history, developmental stages, physical and neurological examination findings were collected. The International Guide for Monitoring Child Development (GMCD) was administered to each child. In addition, semistructured interviews were conducted with the parents using the Vineland Adaptive Behavior Scales, Second edition (Vineland-II).Results: A total of 12 patients diagnosed with WBS via detection of the 7q11.23 deletion, of whom 6 were girls, were retrospectively reviewed. The mean age at the time of review was 54.6 +/- 32.5 months. The mean age at first presentation to the Developmental Behavioral Pediatrics Outpatient Clinic was 15 +/- 11.5 months. In the first developmental evaluation using the GMCD, there was a delay in fine and gross motor domains in 6 patients, in the language domains in 4 patients, and in all of the domains in 2 patients. Findings with Vineland-II showed socialization and communication domains as strengths, but the daily living skills and motor skills domains were weaknesses. In terms of maladaptive behavior, the patients tended to frequently have behavioral problems, neurodevelopmental disease, anxiety disorders, eating problems, and sleeping problems.Conclusion: This retrospective review of 12 patients indicated a general delay in overall development, and confirmed impairment in both adaptive and maladaptive functioning in WBS.
Diurnal variation in Uchikomi fitness test performance: Influence of warm-up protocols
(Frontiers Media Sa, 2022) Eken, Oezguer; Yagin, Fatma Hilal; Eken, Ismihan; Gabrys, Tomasz; Knappova, Vera; Bayrakdaroglu, Serdar; Akyildiz, Zeki
Performance is judged using a variety of methods to ensure uniformity between competitions. Uchikomi Fitness Test (UFT) could accomplished between morning qualifying and evening finals. The purpose of this study is to investigate the impact of different warm-up protocols on UFT at different times of the day in female judokas. Ten volunteer women who had been practising judo on a regular basis for more than 5 years and actively competed in international tournaments took part in this study. Judokas completed UFT, either after no-warm-up (NWU), specific warm-up (SWU), and linear+lateral warm-up (FWU) protocols for two times a day in the morning: 09:00-11:00 and in the evening: 16:00-18:00, on non-consecutive days. In conclusion, there was a significant increase in UFT scores (F = 9.89; p = 0.002), a + b (F = 4.42; p = 0.04) and heart rate (F = 28.99; p < 0.001) in the early evening compared to the morning. Increases in UFT performance were observed in the SWU protocol compared to the NWU and FWU protocols (p < 0.05). However, the interaction between time of day and warm protocol was not significant (p > 0.05). The UFT performance revealed diurnal variation, and the judokas' performances may be favourably affected more in the late hours, particularly following SWU procedures.
Do Oncologists Recommend the Pill of Physical Activity in Their Practice? Answers from the Oncologist and Patients' Perspectives
(Mdpi, 2024) Aguirre-Betolaza, Aitor Martinez; Amezua, Ander Dobaran; Yagin, Fatma Hilal; Cacicedo, Jon; Olasagasti-Ibargoien, Jurgi; Castaneda-Babarro, Arkaitz
Simple Summary Nowadays, everyone is aware of the health benefits of physical activity (PA). In the case of cancer, the evidence is strong in favour of PA, but in many cases, the message does not reach patients. For this reason, in the present study, we wanted to find out the points of view of oncologists and their respective patients with regard to the prescription of PA and the possible causes for the message not passing through adequately. We observed that 97% of oncologists said that they prescribe PA in their office, while only 62% of their patients said that they have received these guidelines. It was also observed that those patients who claimed to have received recommendations for PA were more active in their daily lives, walking more days per week and more minutes per day. This study is the starting point for finding out where the discrepancies between oncologist-patient communication are.Abstract Objectives: The purposes of this current questionnaire-based study were to analyse whether oncologists prescribed PA to their patients in Spain, as well as the type of exercise recommended, the variables that influence whether or not to recommend it and to compare these recommendations with the values reported by their patients. Methods: Two online questionnaires were designed for this study. The first one, filled in by the oncologists (n = 93), contained aspects such as the attitude or barriers to promoting PA. The second was designed for patients with cancer (n = 149), which assessed PA levels and counselling received from oncologists, among other facets. Results: The majority of oncologists (97%) recommend PA during their consultations. Instead, only 62% of patients reported participating in exercise within the last 7 days. Walking was the most common form of exercise, reported by 50% of participants. Patients who received exercise recommendations from their oncologist walked for more days (p = 0.004; ES = 0.442) and more minutes per day (p = 0.022; ES = 0.410). The barriers most highlighted by patients were lack of time and not knowing how to perform PA. Conclusion: Oncologists and patients seem to be interested and able to participate in PA counselling and programmes. However, there was a discrepancy between what was reported by oncologists and expressed by patients in terms of recommendations for PA and the modality itself.
Does body mass index distinguish motor proficiency, social and emotional maturity among adolescent girls?
(Bmc, 2023) Badicu, Georgian; Sani, Seyed Hojjat Zamani; Fathirezaie, Zahra; Esmaeili, Mohaddese; Bassan, Julio Cesar; Gonzalez-Fernandez, Francisco Tomas; Yagin, Fatma Hilal
BackgroundThe objective of this study was to investigate whether different body mass index (BMI) groups could serve as a distinguishing factor for assessing motor proficiency and social and emotional maturity in adolescent girls.Methods140 girls ranging from 12 to 14.5 years old were selected from the schools of Tabriz city, Iran. After their height and weight were measured to calculate body mass index, they completed the following questionnaires: Bruininks-Oseretsky Test of motor proficiency, Second Edition,Vineland Social Maturity Scale, and Emotional Maturity scale.Resultsnormal-weight girls had a meaningful advantage against overweight and underweight participants in the gross motor factor of motor proficiency (p = 0.004), but there wasn't a meaningful difference in the fine motor p = 0.196) and coordination factors (p = 0.417). Also, social maturity showed an advantage of normal and underweight adolescent girls in the self-help dressing factor (p = 0.018), while the locomotion skills (p = 0.010) factor revealed a better performance of normal weight and overweight groups over underweight adolescents. No significant differences were observed in the emotional maturity subscales (p = 0.63) between the groups.ConclusionsThe present study demonstrates that BMI has a direct influence on adolescents' gross motor proficiency and social maturity.
The effect of 8-weeks of combined resistance training and chocolate milk consumption on maximal strength, muscle thickness, peak power and lean mass, untrained, university-aged males
(Frontiers Media Sa, 2023) Yapici, Hakan; Gulu, Mehmet; Yagin, Fatma Hilal; Ugurlu, Dondu; Comertpay, Ertan; Eroglu, Oguz; Kocoglu, Melike
The overarching aim of this study was to investigate the combined effects of chocolate milk consumption (500 mL) with 8-week of resistance training on muscle hypertrophy, body composition, and maximal strength in untrained healthy men. A total of 22 Participants were randomly divided into two experimental groups: combined resistance training (3 sessions per week for 8 weeks) and chocolate milk consumptions (include 30 g protein) Resistance Training Chocolate Milk (RTCM) (Age: 20.9 +/- 0.9 years old) and resistance training (RT) only (Age: 19.8 +/- 0.7 years old). Muscle thickness (MT), using a portable ultrasound, body composition, body mass, maximal strength (one repetition maximum (1 RM), counter movement jump (CMJ) and peak power (PP) were determined at baseline and 8 weeks later. In the RTCM, finding showed a significant improvement in the outcomes compared to the RT group, besides the main effect of time (pre and post). The 1 RM total increased by 36.7% in RTCM group compared to 17.6% increased in the RT group (p < 0.001). Muscle thickness increased by 20.8% in the RTCM group and 9.1% in the RT group (p < 0.001). In the RTCM group, the PP increased by 37.8% compared to only 13.8% increase in the RT group (p = 0.001). The group*time interaction effect was significant for MT, 1RM, CMJ, and PP (p < 0.05), and it was observed that the RTCM and the 8-week resistance training protocol maximized performance. Body fat percentage (%) decreased more in the RTCM (18.9%) group than in the RT (6.7%) group (p = 0.002). In conclusion, chocolate milk (500 mL) with high protein content consumed in addition to resistance training provided superior gains in terms of MT, 1 RM, body composition, CMJ, and PP. The finding of the study demonstrated the positive effect of casein-based protein (chocolate milk) and resistance training on the muscle performance. Chocolate milk consumption has a more positive effect on muscle strength when combined with RT and should be considered as a suitable post-exercise nutritional supplement. Future research could be conducted with a larger number of participants of different ages and longer study durations.
Effect of COVID-19 Pandemic on Patients Who Have Undergone Liver Transplantation: Retrospective Cohort Study
(Mdpi, 2023) Akbulut, Sami; Yagin, Fatma Hilal; Sahin, Tevfik Tolga; Garzali, Ibrahim Umar; Tuncer, Adem; Akyuz, Musap; Bagci, Nazlican
Background: In liver transplant (LT) recipients, immunosuppressive therapy may potentially increase the risk of severe COVID-19 and may increase the mortality in patients. However, studies have shown conflicting results, with various studies reporting poor outcomes while the others show no difference between the LT recipients and healthy population. The aim of this study is to determine the impact of the COVID-19 pandemic on survival of LT recipients. Methods: This is a retrospective cohort study analyzing the data from 387 LT recipients diagnosed with COVID-19. LT recipients were divided into two groups: survival (n = 359) and non-survival (n = 28) groups. A logistic regression model was used to determine the independent risk factors for mortality. Machine learning models were used to analyze the contribution of independent variables to the mortality in LT recipients. Results: The COVID-19-related mortality rate in LT recipients was 7.2%. Multivariate analysis showed that everolimus use (p = 0.012; OR = 6.2), need for intubation (p = 0.001; OR = 38.4) and discontinuation of immunosuppressive therapy (p = 0.047; OR = 7.3) were independent risk factors for mortality. Furthermore, COVID-19 vaccination reduced the risk of mortality by 100 fold and was the single independent factor determining the survival of the LT recipients. Conclusion: The effect of COVID-19 infection on LT recipients is slightly different from the effect of the disease on the general population. The COVID-19-related mortality is lower than the general population and vaccination for COVID-19 significantly reduces the risk of mortality.
The effect of functional remission and cognitive insight on criminal behavior in patients with schizophrenia
(W B Saunders Co-Elsevier Inc, 2023) Polat, Hatice; Ugur, Kerim; Aslanoglu, Eren; Yildiz, Sevler; Yagin, Fatma Hilal
Objective: This study was planned to determine the relationship of functional remission with a criminal history and determine its effect on criminal behavior in patients with schizophrenia.Methods: This cross-sectional study was conducted with 132 patients with schizophrenia (66 with and 66 without a criminal history). Data were collected between November 2020 and April 2021 using a personal information form, the Functional Remission of General Schizophrenia (FROGS), the Taylor Crime Violence Rating Scale, the Beck Cognitive Insight Scale, and the Positive and Negative Syndrome Scale (PANSS) was used to collect data.Results: In terms of all scale variables, there were significant differences between the groups with and without a criminal history (p < 0.05). These differences were mostly clearly observed in the FROGS-social functionality (effect size: 16.79), PANSS-positive (effect size: 2.62) and FROGS-health and treatment (effect size: 2) subscales. Conclusions: In this study, it was determined that as the symptoms of the illness increased in schizophrenia, the patients' functional remission and insight decreased, and their tendency to commit crimes increased. Psychiatric nurses can plan therapeutic interventions to increase the functionality and insight levels of patients with schizophrenia.

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