Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome.

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.

Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study.

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.

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model.

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.

Determination of biomarker candidates for the placenta accreta spectrum by plasma proteomic analysis.

Placenta accreta spectrum (PAS) presents a significant obstetric challenge, associated with considerable maternal and fetal-neonatal morbidity and mortality. Nevertheless, it is imperative to acknowledge that a noteworthy subset of PAS cases remains undetected until the time of delivery, thereby contributing to an augmented incidence of morbidity among the affected individuals. The delayed identification of PAS not only hinders timely intervention but also exacerbates the associated health risks for both the maternal and fetal outcomes. This underscores the urgency to innovate strategies for early PAS diagnosis. In this study, we aimed to explore plasma proteins as potential diagnostic biomarkers for PAS. Integrated transcriptome and proteomic analyses were conducted to establish a novel diagnostic approach. A cohort of 15 pregnant women diagnosed with PAS and delivering at Inonu University Faculty of Medicine between 01/04/2021 and 01/01/2023, along with a matched control group of 15 pregnant women without PAS complications, were enrolled. Plasma protein identification utilized enzymatic digestion and liquid chromatography-tandem mass spectrometry techniques. Proteomic analysis identified 228 plasma proteins, of which 85 showed significant differences (P < 0.001) between PAS and control cases. We refined this to a set of 20 proteins for model construction, resulting in a highly accurate classification model (96.9% accuracy). Notable associations were observed for proteins encoded by P01859 (Immunoglobulin heavy constant gamma 2), P02538 (Keratin type II cytoskeletal 6A), P29622 [Kallistatin (also known as Serpin A4)], P17900 (Ganglioside GM2 activator Calmodulin-like protein 5), and P01619 (Immunoglobulin kappa variable 3-20), with fold changes indicating their relevance in distinguishing PAS from control groups. In conclusion, our study has identified novel plasma proteins that could serve as potential biomarkers for early diagnosis of PAS in pregnant women. Further research and validation in larger PAS cohorts are necessary to determine the clinical utility and reliability of these proteomic biomarkers for diagnosing PAS.

A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris.

Cardiovascular diseases (CVDs) are a serious public health issue that affects and is responsible for numerous fatalities and impairments. Ischemic heart disease (IHD) is one of the most prevalent and deadliest types of CVDs and is responsible for 45% of all CVD-related fatalities. IHD occurs when the blood supply to the heart is reduced due to narrowed or blocked arteries, which causes angina pectoris (AP) chest pain. AP is a common symptom of IHD and can indicate a higher risk of heart attack or sudden cardiac death. Therefore, it is important to diagnose and treat AP promptly and effectively. To forecast AP in women, we constructed a novel artificial intelligence (AI) method employing the tree-based algorithm known as an Explainable Boosting Machine (EBM). EBM is a machine learning (ML) technique that combines the interpretability of linear models with the flexibility and accuracy of gradient boosting. We applied EBM to a dataset of 200 female patients, 100 with AP and 100 without AP, and extracted the most relevant features for AP prediction. We then evaluated the performance of EBM against other AI methods, such as Logistic Regression (LR), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM). We found that EBM was the most accurate and well-balanced technique for forecasting AP, with accuracy (0.925) and Youden's index (0.960). We also looked at the global and local explanations provided by EBM to better understand how each feature affected the prediction and how each patient was classified. Our research showed that EBM is a useful AI method for predicting AP in women and identifying the risk factors related to it. This can help clinicians to provide personalized and evidence-based care for female patients with AP.

An Explainable Artificial Intelligence Model Proposed for the Prediction of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and the Identification of Distinctive Metabolites.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating illness with a significant global prevalence, affecting over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. This study uses explainable artificial intelligence and machine learning techniques to identify discriminative metabolites for ME/CFS. MATERIAL AND METHODS: The model investigates a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics. Random forest methods together with other classifiers were applied to the data to classify individuals as ME/CFS patients and healthy individuals. The classification learning algorithms' performance in the validation step was evaluated using a variety of methods, including the traditional hold-out validation method, as well as the more modern cross-validation and bootstrap methods. Explainable artificial intelligence approaches were applied to clinically explain the optimum model's prediction decisions. RESULTS: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis. The random forest model outperformed the other classifiers in ME/CFS prediction using the 1000-iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC. According to the obtained results, the bootstrap validation approach demonstrated the highest classification outcomes. CONCLUSION: The proposed model accurately classifies ME/CFS patients based on the selected biomarker candidate metabolites. It offers a clear interpretation of risk estimation for ME/CFS, aiding physicians in comprehending the significance of key metabolomic features within the model.

Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research.

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.

Artificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is the main cause of mortality from cancer globally. This paper intends to classify public gene expression data of patients with Hepatitis C virus-related HCC (HCV+HCC) and chronic HCV without HCC (HCV alone) through the XGboost approach and to identify key genes that may be responsible for HCC. Methods: The current research is a retrospective case-control study. Public data from 17 patients with HCV+HCC and 35 patients with HCV-alone samples were used in this study. An XGboost model was established for the classification by 10-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were utilized for performance assessment. Results: AC, BAC, sensitivity, specificity, positive predictive value, negative predictive value, and F1 scores from the XGboost model were 98.1, 97.1, 100, 94.1, 97.2, 100, and 98.6%, respectively. According to the variable importance values from the XGboost, the HAO2, TOMM20, GPC3, and PSMB4 genes can be considered potential biomarkers for HCV-related HCC. Conclusion: A machine learning-based prediction method discovered genes that potentially serve as biomarkers for HCV-related HCC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established. Additionally, more detailed clinical works are needed to substantiate the significant conclusions in the current study.

Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits.

Obesity is the excessive accumulation of adipose tissue in the body that leads to health risks. The study aimed to classify obesity levels using a tree-based machine-learning approach considering physical activity and nutritional habits. Methods: The current study employed an observational design, collecting data from a public dataset via a web-based survey to assess eating habits and physical activity levels. The data included gender, age, height, weight, family history of being overweight, dietary patterns, physical activity frequency, and more. Data preprocessing involved addressing class imbalance using Synthetic Minority Over-sampling TEchnique-Nominal Continuous (SMOTE-NC) and feature selection using Recursive Feature Elimination (RFE). Three classification algorithms (logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost)) were used for obesity level prediction, and Bayesian optimization was employed for hyperparameter tuning. The performance of different models was evaluated using metrics such as accuracy, recall, precision, F1-score, area under the curve (AUC), and precision-recall curve. The LR model showed the best performance across most metrics, followed by RF and XGBoost. Feature selection improved the performance of LR and RF models, while XGBoost's performance was mixed. The study contributes to the understanding of obesity classification using machine-learning techniques based on physical activity and nutritional habits. The LR model demonstrated the most robust performance, and feature selection was shown to enhance model efficiency. The findings underscore the importance of considering both physical activity and nutritional habits in addressing the obesity epidemic.

Predicting Duodenal Cancer Risk in Patients with Familial Adenomatous Polyposis Using Machine Learning Model.

BACKGROUND/AIMS: The aim of this study was to both classify data of familial adenomatous polyposis patients with and without duode- nal cancer and to identify important genes that may be related to duodenal cancer by XGboost model. MATERIALS AND METHODS: The current study was performed using expression profile data from a series of duodenal samples from familial adenomatous polyposis patients to explore variations in the familial adenomatous polyposis duodenal adenoma-carcinoma sequence. The expression profiles obtained from cancerous, adenomatous, and normal tissues of 12 familial adenomatous polyposis patients with duodenal cancer and the tissues of 12 familial adenomatous polyposis patients without duodenal cancer were compared. The ElasticNet approach was utilized for the feature selection. Using 5-fold cross-validation, one of the machine learning approaches, XGboost, was utilized to classify duodenal cancer. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score performance metrics were assessed for model performance. RESULTS: According to the variable importance obtained from the modeling, ADH1C, DEFA5, CPS1, SPP1, DMBT1, VCAN-AS1, APOB genes (cancer vs. adenoma); LOC399753, APOA4, MIR548X, and ADH1C genes (adenoma vs. adenoma); SNORD123, CEACAM6, SNORD78, ANXA10, SPINK1, and CPS1 (normal vs. adenoma) genes can be used as predictive biomarkers. CONCLUSIONS: The proposed model used in this study shows that the aforementioned genes can forecast the risk of duodenal cancer in patients with familial adenomatous polyposis. More comprehensive analyses should be performed in the future to assess the reliability of the genes determined.

Effect of COVID-19 Pandemic on Patients Who Have Undergone Liver Transplantation: Retrospective Cohort Study.

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.

Evaluation of Nurses' Vaccine Hesitancy, Psychological Resilience, and Anxiety Levels During COVID-19 Pandemic.

OBJECTIVE: This study aimed to evaluate the vaccine hesitancy, psychological resilience, and anxiety levels of nurses during the COVID-19 pandemic. MATERIALS AND METHODS: This cross-sectional study was conducted with 676 nurses working at the survey time. Sociodemographic features, the status of hesitancy against the COVID-19 vaccine, the Coronavirus Anxiety Scale, and the Brief Resilience Scale were used in the questionnaire form to collect the data. RESULTS: Most participants (68.6%; n=464) stated they were hesitant about the COVID-19 vaccine. A sig- nificantly higher rate of hesitancy was detected in the age group of 20-39 years, those who did not have COVID-19 vaccine, and those who did not think the COVID-19 vaccine is protective (P < .05). It was determined that 6.8% (n=46) of the nurses had COVID-19 anxiety. A significantly higher rate of anxiety was detected in the age group of 40 years and older, those working in the emergency department, and those working in the COVID-19 unit during the pandemic period (P < .05). The median Brief Resilience Scale score of nurses is 19(6). A negative, weak, and significant relationship was found between the Brief Resilience Scale and Coronavirus Anxiety Scale scores (P = .001). CONCLUSION: During the pandemic, higher rates of anxiety were detected in healthcare personnel and those working in COVID-19 units. It was also found that as the level of anxiety increased, the level of psychological resilience decreased. To reduce the anxiety level and strengthen the psychological resilience of nurses, the cornerstones of the health system, fast, effective, and curative interventions should be made.

Value of fecal calprotectin in prediction of acute appendicitis based on a proposed model of machine learning.

BACKGROUND: The aim of this study is to apply random forest (RF), one of the machine learning (ML) algorithms, to a dataset consisting of patients with a presumed diagnosis of acute appendicitis (AAp) and to reveal the most important factors associated with the diagnosis of AAp based on the variable importance. METHODS: An open-access dataset comparing two patient groups with (n=40) and without (n=44) AAp to predict biomarkers for AAp was used for this case-control study. RF was used for modeling the data set. The data were divided into two training and test dataset (80: 20). Accuracy, balanced accuracy (BC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) performance metrics were appraised for model performance. RESULTS: Accuracy, BC, sensitivity, specificity, PPV, NPV, and F1 scores pertaining to the RF model were 93.8%, 93.8%, 87.5%, 100%, 100%, 88.9%, and 93.3%, respectively. Following the variable importance values regarding the model, the variables most associated with the diagnosis and prediction of AAp were fecal calprotectin (100 %), radiological imaging (89.9%), white blood test (51.8%), C-reactive protein (47.1%), from symptoms onset to the hospital visit (19.3%), patients age (18.4%), alanine aminotransferase levels >40 (<1%), fever (<1%), and nausea/vomiting (<1%), respectively. CONCLUSION: A prediction model was developed for AAp with the ML method in this study. Thanks to this model, biomarkers that predict AAp with high accuracy were determined. Thus, the decision-making process of clinicians for diagnosing AAp will be facilitated, and the risks of perforation and unnecessary operations will be minimized thanks to the timely diagnosis with high accuracy.

Modeling Based on Ensemble Learning Methods for Detection of Diagnostic Biomarkers from LncRNA Data in Rats Treated with Cis-Platinum-Induced Hepatotoxicity.

BACKGROUND: The first aim of this study is to perform bioinformatic analysis of lncRNAs obtained from liver tissue samples from rats treated with cisplatin hepatotoxicity and without pathology. Another aim is to identify possible biomarkers for the diagnosis/early diagnosis of hepatotoxicity by modeling the data obtained from bioinformatics analysis with ensemble learning methods. METHODS: In the study, 20 female Sprague-Dawley rats were divided into a control group and a hepatotoxicity group. Liver samples were taken from rats, and transcriptomic and histopathological analyses were performed. The dataset achieved from the transcriptomic analysis was modeled with ensemble learning methods (stacking, bagging, and boosting). Modeling results were evaluated with accuracy (Acc), balanced accuracy (B-Acc), sensitivity (Se), specificity (Sp), positive predictive value (Ppv), negative predictive value (Npv), and F1 score performance metrics. As a result of the modeling, lncRNAs that could be biomarkers were evaluated with variable importance values. RESULTS: According to histopathological and immunohistochemical analyses, a significant increase was observed in the sinusoidal dilatation and Hsp60 immunoreactivity values in the hepatotoxicity group compared to the control group (p < 0.0001). According to the results of the bioinformatics analysis, 589 lncRNAs showed different expressions in the groups. The stacking model had the best classification performance among the applied ensemble learning models. The Acc, B-Acc, Se, Sp, Ppv, Npv, and F1-score values obtained from this model were 90%, 90%, 80%, 100%, 100%, 83.3%, and 88.9%, respectively. lncRNAs with id rna-XR_005492522.1, rna-XR_005492536.1, and rna-XR_005505831.1 with the highest three values according to the variable importance obtained as a result of stacking modeling can be used as predictive biomarker candidates for hepatotoxicity. CONCLUSIONS: Among the ensemble algorithms, the stacking technique yielded higher performance results as compared to the bagging and boosting methods on the transcriptomic data. More comprehensive studies can support the possible biomarkers determined due to the research and the decisive results for the diagnosis of drug-induced hepatotoxicity.

Effect of the COVID-19 Pandemic on the Psychological Health of Patients Who Underwent Liver Transplantation Due to Hepatocellular Carcinoma.

BACKGROUND: The primary aim of this study was to compare liver transplant (LT) recipients with and without hepatocellular carcinoma (HCC) in terms of COVID-19-related depression, anxiety, and stress. METHOD: A total of 504 LT recipients with (HCC group; n = 252) and without HCC (non-HCC group; n = 252) were included in the present case-control study. Depression Anxiety Stress Scales (DASS-21) and Coronavirus Anxiety Scale (CAS) were used to evaluate the depression, stress, and anxiety levels of LT patients. DASS-21 total and CAS-SF scores were determined as the primary outcomes of the study. Poisson regression and negative binomial regression models were used to predict the DASS and CAS scores. The incidence rate ratio (IRR) was used as a coefficient. Both groups were also compared in terms of awareness of the COVID-19 vaccine. RESULTS: Poisson regression and negative binomial regression analyses for DASS-21 total and CAS-SF scales showed that the negative binomial regression method was the appropriate model for both scales. According to this model, it was determined that the following independent variables increased the DASS-21 total score: non-HCC (IRR: 1.26; p = 0.031), female gender (IRR: 1.29; p = 0.036), presence of chronic disease (IRR: 1.65; p < 0.001), exposure to COVID-19 (IRR: 1.63; p < 0.001), and nonvaccination (IRR: 1.50; p = 0.002). On the other hand, it was determined that the following independent variables increased the CAS score: female gender (IRR:1.75; p = 0.014) and exposure to COVID-19 (IRR: 1.51; p = 0.048). Significant differences were found between the HCC and non-HCC groups in terms of median DASS-21 total (p < 0.001) and CAS-SF (p = 0.002) scores. Cronbach's alpha internal consistency coefficients of DASS-21 total and CAS-SF scales were calculated to be 0.823 and 0.783, respectively. CONCLUSION: This study showed that the variables including patients without HCC, female gender, having a chronic disease, being exposed to COVID-19, and not being vaccinated against COVID-19 increased anxiety, depression, and stress. High internal consistency coefficients obtained from both scales indicate that these results are reliable.

A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients.

Colorectal cancer (CRC) is one of the most common and lethal diseases among all types of cancer, and metabolites play a significant role in the development of this complex disease. This study aimed to identify potential biomarkers and targets in the diagnosis and treatment of CRC using high-throughput metabolomics. Metabolite data extracted from the feces of CRC patients and healthy volunteers were normalized with the median normalization and Pareto scale for multivariate analysis. Univariate ROC analysis, the t-test, and analysis of fold changes (FCs) were applied to identify biomarker candidate metabolites in CRC patients. Only metabolites that overlapped the two different statistical approaches (false-discovery-rate-corrected p-value < 0.05 and AUC > 0.70) were considered in the further analysis. Multivariate analysis was performed with biomarker candidate metabolites based on linear support vector machines (SVM), partial least squares discrimination analysis (PLS-DA), and random forests (RF). The model identified five biomarker candidate metabolites that were significantly and differently expressed (adjusted p-value < 0.05) in CRC patients compared to healthy controls. The metabolites were succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine. Aminoisobutyric acid was the metabolite with the highest discriminatory potential in CRC, with an AUC equal to 0.806 (95% CI = 0.700-0.897), and was down-regulated in CRC patients. The SVM model showed the most substantial discrimination capacity for the five metabolites selected in the CRC screening, with an AUC of 0.985 (95% CI: 0.94-1).

Effects of intracerebroventricular MOTS-c infusion on thyroid hormones and uncoupling proteins.

This study was conducted to determine the possible effects of intracerebroventricular MOTS-c infusion on thyroid hormones and uncoupling proteins (UCPs) in rats. Forty male Wistar Albino rats were divided into 4 groups with 10 animals in each group: control, sham, 10 and 100 µM MOTS-c. Hypothalamus, blood, muscle, adipose tissues samples were collected for thyrotropin-releasing hormone (TRH), UCP1 and UCP3 levels were determined by the RT-PCR and western blot analysis. Serum thyroid hormone levels were determined by the ELISA assays. MOTS-c infusion was found to increase food consumption but it did not cause any changes in the body weight. MOTS-c decreased serum TSH, T3, and T4 hormone levels. On the other hand, it was also found that MOTS-c administration increased UCP1 and UCP3 levels in peripheral tissues. The findings obtained in the study show that central MOTS-c infusion is a directly effective agent in energy metabolism.

Evaluation of the Turkish Population's Perspective on COVID-19 Vaccine Hesitancy and Routine Childhood Vaccine Applications: National Survey Study.

BACKGROUND: It is important to evaluate the attitude of society towards vaccines to understand the rates of acceptance and hesitance towards vaccination, which are essential components of public health and epidemiology. This study aimed to evaluate the perspective of the Turkish population on COVID-19 status, rate of vaccination, and also to evaluate the reasons for refusal to vaccinate, vaccine hesitancy, and related factors. METHODS: A total of 4539 participants were included in this population-based descriptive and cross-sectional study. The Nomenclature of Territorial Units for Statistics (NUTS-II) was used to obtain a representative sample and for this purpose Turkey was divided into 26 regions. Participants were randomly selected based on the demographic features and population ratios of the selected regions. The following parameters were evaluated: sociodemographic characteristics and perspectives on COVID-19 vaccines, Vaccine Hesitancy Scale Adapted to Pandemics (VHS-P), and Anti-Vaccine Scale-Long Form (AVS-LF) questions. RESULTS: A total of 4539 participants, 2303 (50.7%) male and 2236 (49.3%) female, aged between 18 and 73 years, were included in this study. It was observed that 58.4% of the participants had hesitations towards COVID-19 vaccination, and 19.6% were hesitant about all childhood vaccinations. Those who did not have the COVID-19 vaccine, who did not think that the COVID-19 vaccine was protective, and who had hesitation to vaccinate against COVID-19 had significantly higher median scores on the VHS-P and AVS-LF scales, respectively (all p < 0.01). Those who did not have their children vaccinated in childhood and who were hesitant about childhood vaccinations, had significantly higher median scores on the VHS-P and AVS-LF scales, respectively (all p < 0.01). CONCLUSION: Although the rate of vaccination for COVID-19 was 93.4% in the study, hesitation to vaccinate was 58.4%. The median score of the scales of those who were hesitant about childhood vaccinations was higher than individuals who did not have any hesitation. In general, the source of concerns about vaccines should be clearly seen, and precautions should be taken.

Angiotensin II type 2 receptor agonist treatment of doxorubicin induced heart failure.

Doxorubicin (DOX) is an anthracycline derivative used for treatment of malignancies; however, its clinical use is limited by its cardiotoxicity. We investigated the effects of angiotensin II type 2 receptor agonist compound 21 (C21) on DOX induced heart failure in rat heart. We compared C21 with losartan (LOS), an AT 1 receptor antagonist used for treating heart failure. We allocated 40 rats into five groups of eight: saline treated control group, DOX group administered a single 20 mg/kg dose of DOX, DOX + C21 group administered 0.3 mg/kg C21 for 21 days following the 20 mg/kg dose of DOX, DOX + losartan (LOS) group administered a 21 day regimen of 20 mg/kg LOS following the single dose of DOX, and a DOX + LOS + C21 group administered 0.3 mg/kg C21 and 20 mg/kg LOS for 21 days following the single dose of DOX. We assessed histopathology and conducted echocardiograpic and hemodynamic measurements. Left ventricular ejection fraction (EF) was reduced only in the DOX treated group. C21, LOS and C21 + LOS therapy prevented decreased EF due to DOX. Less histopathology was observed in the DOX + LOS + C21 group than for the other treatment groups. Application of C21 decreased DOX induced cardiac injury similar to LOS. Combined use of C21 and LOS was most beneficial for DOX induced heart failure.

Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence.

BACKGROUND: The primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI). METHOD: A total of 1797 patients who underwent appendectomy with a preliminary diagnosis of AAp between May 2009 and March 2022 were included in the study. Considering the histopathological examination, the patients were divided into two groups as AAp (n = 1465) and non-AAp (NA; n = 332); the non-AAp group is also referred to as negative appendectomy. Subsequently, patients confirmed to have AAp were divided into two subgroups: nonperforated (n = 1161) and perforated AAp (n = 304). The missing values in the data set were assigned using the Random Forest method. The Boruta variable selection method was used to identify the most important variables associated with AAp and perforated AAp. The class imbalance problem in the data set was resolved by the SMOTE method. The CatBoost model was used to classify AAp and non-AAp patients and perforated and nonperforated AAp patients. The performance of the model in the holdout test set was evaluated with accuracy, F1- score, sensitivity, specificity, and area under the receiver operator curve (AUC). The SHAP method, which is one of the XAI methods, was used to interpret the model results. RESULTS: The CatBoost model could distinguish AAp patients from non-AAp individuals with an accuracy of 88.2% (85.6-90.8%), while distinguishing perforated AAp patients from nonperforated AAp individuals with an accuracy of 92% (89.6-94.5%). According to the results of the SHAP method applied to the CatBoost model, it was observed that high total bilirubin, WBC, Netrophil, WLR, NLR, CRP, and WNR values, and low PNR, PDW, and MCV values increased the prediction of AAp biochemically. On the other hand, high CRP, Age, Total Bilirubin, PLT, RDW, WBC, MCV, WLR, NLR, and Neutrophil values, and low Lymphocyte, PDW, MPV, and PNR values were observed to increase the prediction of perforated AAp. CONCLUSION: For the first time in the literature, a new approach combining ML and XAI methods was tried to predict AAp and perforated AAp, and both clinical conditions were predicted with high accuracy. This new approach proved successful in showing how well which demographic and biochemical parameters could explain the current clinical situation in predicting AAp and perforated AAp.