Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics

Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) % accuracy, (89.33 ± 1.80) % precision, (91.24 ± 1.67) % recall, (89.37 ± 1.52) % F1-Score, and (97.00 ± 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies

The impact of enriched environments on cerebral oxidative balance in rodents: a systematic review of environmental variability effects

IntroductionThe present review aimed to systematically summarize the impacts of environmental enrichment (EE) on cerebral oxidative balance in rodents exposed to normal and unfavorable environmental conditions.MethodsIn this systematic review, four databases were used: PubMed (830 articles), Scopus (126 articles), Embase (127 articles), and Science Direct (794 articles). Eligibility criteria were applied based on the Population, Intervention, Comparison, Outcomes, and Study (PICOS) strategy to reduce the risk of bias. The searches were carried out by two independent researchers; in case of disagreement, a third participant was requested. After the selection and inclusion of articles, data related to sample characteristics and the EE protocol (time of exposure to EE, number of animals, and size of the environment) were extracted, as well as data related to brain tissues and biomarkers of oxidative balance, including carbonyls, malondialdehyde, nitrotyrosine, oxygen-reactive species, and glutathione (reduced/oxidized).ResultsA total of 1,877 articles were found in the four databases, of which 16 studies were included in this systematic review. The results showed that different EE protocols were able to produce a global increase in antioxidant capacity, both enzymatic and non-enzymatic, which are the main factors for the neuroprotective effects in the central nervous system (CNS) subjected to unfavorable conditions. Furthermore, it was possible to notice a slowdown in neural dysfunction associated with oxidative damage, especially in the prefrontal structure in mice.DiscussionIn conclusion, EE protocols were determined to be valid tools for improving oxidative balance in the CNS. The global decrease in oxidative stress biomarkers indicates refinement in reactive oxygen species detoxification, triggering an improvement in the antioxidant network

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 ofMLand XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS

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

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

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

Zvýšení výkonu při anaerobních testech rychlosti po třicetiminutovém spánku během a po ramadánu.

Účelem této studie bylo určit dopad 30minutového zdřímnutí (N30) na běžecký anaerobní sprintový test (RAST) jak během ramadánu, tak po něm. Deset fyzicky aktivních kickboxerů (věk: 21,20 +/- 1,61 let, výška: 174,80 +/- 4,34 cm, tělesná hmotnost: 73,30 +/- 7,10 kg a index tělesné hmotnosti (BMI): 24,00 +/- 2,21 kg/m(2) )) dobrovolně provedlo test RAST po N30 a ve stavu bez spánku (NN) během dvou experimentálních období: posledních deset dnů ramadánu (DR) a podobně jako 3 týdny po ramadánu (AR). Během každého protokolu DR-NN, DR-N30, AR-NN a AR-N30 předváděli kickboxeři výkon RAST. Mezi obdobími ramadánu (DR vs. AR) byl zjištěn statisticky významný rozdíl z hlediska maximálního výkonu (W) (F = 80,93; p(1) < 0,001; eta(2)(p) = 0,89), minimálního výkonu (W ) (F = 49,05; p(1) < 0,001; eta(2)(p) = 0,84), průměrný výkon (W) (F = 83,79; p(1) < 0,001; eta(2)(p) = 0,90 ) a výsledky indexu únavy (%) (F = 11,25; p(1) = 0,008; eta(2)(p) = 0,55). Kromě toho byl faktor zdřímnutí statisticky významný z hlediska maximálního výkonu (W) (F = 81,89; p(2) < 0,001; eta(2)(p) = 0,90), minimálního výkonu (W) (F = 80,37 p(2) < 0,001; eta(2)(p) = 0,89), průměrný výkon (W) (F = 108,41; p(2) < 0,001; eta(2)(p) = 0,92) a index únavy ( % výsledků (F = 16,14; p(2) = 0,003; eta(2)(p) = 0,64). Denní spánek prospívá následnému výkonu v RAST. Výhody zdřímnutí byly větší po příležitosti N30 pro DR a AR.The purpose of this study was to determine the impact of a 30 min nap (N30) on the Running-Based Anaerobic Sprint Test (RAST) both during and after Ramadan. Ten physically active kickboxers (age: 21.20 +/- 1.61 years, height: 174.80 +/- 4.34 cm, body mass: 73.30 +/- 7.10 kg and body mass index (BMI): 24.00 +/- 2.21 kg/m(2)) voluntarily performed the RAST test after an N30 and in a no-nap condition (NN) during two experimental periods: the last ten days of Ramadan (DR) and similar to 3 weeks after Ramadan (AR). During each DR-NN, DR-N30, AR-NN and AR-N30 protocol, kickboxers performed RAST performance. A statistically significant difference was found between Ramadan periods (DR vs. AR) in terms of max power (W) (F = 80.93; p(1) < 0.001; eta(2)(p) = 0.89), minimum power (W) (F = 49.05; p(1) < 0.001; eta(2)(p) = 0.84), average power (W) (F = 83.79; p(1) < 0.001; eta(2)(p) = 0.90) and fatigue index (%) results (F = 11.25; p(1) = 0.008; eta(2)(p) = 0.55). In addition, the nap factor was statistically significant in terms of the max power (W) (F = 81.89; p(2) < 0.001; eta(2)(p) = 0.90), minimum power (W) (F = 80.37; p(2) < 0.001; eta(2)(p) = 0.89), average power (W) (F = 108.41; p(2) < 0.001; eta(2)(p) = 0.92) and fatigue index (%) results (F = 16.14; p(2) = 0.003; eta(2)(p) = 0.64). Taking a daytime nap benefits subsequent performance in RAST. The benefits of napping were greater after an N30 opportunity for DR and AR

Zvýšení výkonu při anaerobních testech rychlosti po třicetiminutovém spánku během a po ramadánu.

Účelem této studie bylo určit dopad 30minutového zdřímnutí (N30) na běžecký anaerobní sprintový test (RAST) jak během ramadánu, tak po něm. Deset fyzicky aktivních kickboxerů (věk: 21,20 +/- 1,61 let, výška: 174,80 +/- 4,34 cm, tělesná hmotnost: 73,30 +/- 7,10 kg a index tělesné hmotnosti (BMI): 24,00 +/- 2,21 kg/m(2) )) dobrovolně provedlo test RAST po N30 a ve stavu bez spánku (NN) během dvou experimentálních období: posledních deset dnů ramadánu (DR) a podobně jako 3 týdny po ramadánu (AR). Během každého protokolu DR-NN, DR-N30, AR-NN a AR-N30 předváděli kickboxeři výkon RAST. Mezi obdobími ramadánu (DR vs. AR) byl zjištěn statisticky významný rozdíl z hlediska maximálního výkonu (W) (F = 80,93; p(1) < 0,001; eta(2)(p) = 0,89), minimálního výkonu (W ) (F = 49,05; p(1) < 0,001; eta(2)(p) = 0,84), průměrný výkon (W) (F = 83,79; p(1) < 0,001; eta(2)(p) = 0,90 ) a výsledky indexu únavy (%) (F = 11,25; p(1) = 0,008; eta(2)(p) = 0,55). Kromě toho byl faktor zdřímnutí statisticky významný z hlediska maximálního výkonu (W) (F = 81,89; p(2) < 0,001; eta(2)(p) = 0,90), minimálního výkonu (W) (F = 80,37 p(2) < 0,001; eta(2)(p) = 0,89), průměrný výkon (W) (F = 108,41; p(2) < 0,001; eta(2)(p) = 0,92) a index únavy ( % výsledků (F = 16,14; p(2) = 0,003; eta(2)(p) = 0,64). Denní spánek prospívá následnému výkonu v RAST. Výhody zdřímnutí byly větší po příležitosti N30 pro DR a AR.The purpose of this study was to determine the impact of a 30 min nap (N30) on the Running-Based Anaerobic Sprint Test (RAST) both during and after Ramadan. Ten physically active kickboxers (age: 21.20 +/- 1.61 years, height: 174.80 +/- 4.34 cm, body mass: 73.30 +/- 7.10 kg and body mass index (BMI): 24.00 +/- 2.21 kg/m(2)) voluntarily performed the RAST test after an N30 and in a no-nap condition (NN) during two experimental periods: the last ten days of Ramadan (DR) and similar to 3 weeks after Ramadan (AR). During each DR-NN, DR-N30, AR-NN and AR-N30 protocol, kickboxers performed RAST performance. A statistically significant difference was found between Ramadan periods (DR vs. AR) in terms of max power (W) (F = 80.93; p(1) < 0.001; eta(2)(p) = 0.89), minimum power (W) (F = 49.05; p(1) < 0.001; eta(2)(p) = 0.84), average power (W) (F = 83.79; p(1) < 0.001; eta(2)(p) = 0.90) and fatigue index (%) results (F = 11.25; p(1) = 0.008; eta(2)(p) = 0.55). In addition, the nap factor was statistically significant in terms of the max power (W) (F = 81.89; p(2) < 0.001; eta(2)(p) = 0.90), minimum power (W) (F = 80.37; p(2) < 0.001; eta(2)(p) = 0.89), average power (W) (F = 108.41; p(2) < 0.001; eta(2)(p) = 0.92) and fatigue index (%) results (F = 16.14; p(2) = 0.003; eta(2)(p) = 0.64). Taking a daytime nap benefits subsequent performance in RAST. The benefits of napping were greater after an N30 opportunity for DR and AR

The level of the aggression in karate athletes with different handedness and belt grades

Karate athletes with different lateral talents possess different functions in terms of skills and personality characteristics in a way that handedness can be considered an advantage. Given that there is a paucity of research in the domain of personality characteristics, handedness and belt grades, the current research aims to investigate the relationship between handedness and belt grades with aggression among karate athletes. 120 male karate athletes participated. To measure handedness, we used Annette’s handedness questionnaire and to measure aggression, we used Bredemeier’s aggression questionnaire. The questionnaires were distributed among participants one day before the tournament. A two-way analysis of variance (ANOVA) was used to measure the effects of belt grades and handedness on the level of aggression. The results of the study indicated that there was no statistically significant difference in the average level of aggression between left-handed and right-handed karate athletes. There was also no statistically significant difference in the average level of aggression between karate athletes with different belt grades