Do Oncologists Recommend the "Pill" of Physical Activity in Their Practice? Answers from the Oncologist and Patients' Perspectives.

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.

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

Introduction: The 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. Methods: In 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). Results: A 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. Discussion: In 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 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.

Emotional and Social Outcomes of the Teaching Personal and Social Responsibility Model in Physical Education: A Systematic Review and Meta-Analysis.

CONTEXT: In today's ever-changing world, fostering personal and social responsibility is essential for building strong and compassionate communities. This study aimed to provide a quantitative synthesis focusing on the emotional and social outcomes of Teaching Personal and Social Responsibility (TPSR) model-based Physical Education (PE) programs. METHODS: A comprehensive literature review covering the period from November 2022 to September 2023 identified 637 articles published between 2005 and 2023. Of these, 20 met the inclusion criteria. Data from these articles were coded, and a comprehensive meta-analysis was conducted, incorporating 28 effect sizes. Methodological quality was assessed using the Medical Education Research Study Quality Instrument. Hedge's g served as the effect size measure and emotional and social outcomes subgroups were consolidated. Heterogeneity was evaluated with Cochran's Q and I2. Meta-regression and ANOVA-like models addressed categorical moderators, whereas publication bias was assessed through funnel plot, failsafe number, and Egger's linear regression. RESULTS: A significant and positive effect of the TPSR model on product outcomes (Hedge's g = 0.337, 95% CI = 0.199 to 0.476) was found. Despite considerable heterogeneity (I2 = 83.830), a random effects model was justified. Assessment of publication bias indicated a low likelihood. Moderator analyses revealed that publication countries significantly influenced the effect, with stronger effects in Turkey. Publication type (article vs. thesis) also played roles in moderation. The meta-regression analyses did not reveal significant effects for the grade level, duration of intervention, publication year or sample size on the TPSR model's impact on product outcomes. The TPSR model positively impacts emotional and social outcomes in PE, enhancing children' skills and behaviour. However, variations across cultures highlight the need for further research, considering limitations like language constraints and potential biases in study selection and data extraction.

Shared Reading of the Families With the Children With Chronic Disease.

The aim of the study is to evaluate shared reading of families of children with chronic diseases. The mothers of children aged 2 to 6 years with chronic health problems who applied to the pediatric outpatient clinic between January and May 2022 were the study group, and the mothers of children with an acute health problem were the control group. The sociodemographic information form and "Child-Parent Shared Reading Activities Scale" were applied. At the end of the interview, 3 questions about shared reading were asked. A total of 187 children were enrolled in the study: 92 and 95 in the chronic disease group and control group, respectively. 57.6% of mothers of chronically ill children reported that the parents almost never did shared reading with their child. It was found that all mothers knew the importance of reading, but they could not support especially in the risky chronic disease group.

Personality and motivation of physical activity in adolescent girls: effects of perceived parental support and social physical anxiety.

In the present study, we investigated the relationship between personality and motivation for physical activity while introducing perceived parental support and social physical anxiety in adolescent girls (N = 318, Mage: 16.19 ± 0.51 years). The present study was a retrospective correlational study that was conducted to analyze of a path model. Dark triad traits: Machiavellianism, narcissism, and psychopathy, student's motivation for physical activity, social physique anxiety, and participants' perceptions of parents' behaviors were measured. The findings indicated that psychopathy and Machiavellianism were directly and indirectly associated with motivation for physical activity, but Narcissism could only directly predict the motivation for physical activity. Also, need-thwarting (the most), need-supportive and social physical anxiety could predict motivation for physical activity. This model of the result suggests that among adolescent girls, dark triad personality could, directly and indirectly, predict motivation with need-supportive and need-thwarting and also social physical anxiety. It seems that the sense of importance and more attention to oneself in adolescent girls, which exists in the narcissistic personality, can directly lead to more motivation for physical activity. Also, the duplicitous ways of Machiavellian people in pursuing their motives were confirmed in this research.

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.

Investigation of sports participation motivation in people with disabilities: a cross-sectional study of individuals with physical and hearing impairments.

While reading the literature, it is seen that there are not enough studies on the motivation of disabled individuals to participate in sports. This study aims to examine the sports participation motivations of hearing impaired and physically disabled athletes. This study was a cross-sectional study. The research group of the study consists of physically and hearing-impaired individuals between the ages of 18-47. The participants of this research group consisted of 253 volunteer disabled individuals, 150 of whom were men and 103 of whom were women. Sports participation motivation scale was used for disabled individuals. The scale consists of 3 dimensions and is a 5-point Likert type. The results of the study showed that hearing-impaired people have a higher high school rate and physically person with disability have a higher bachelor's degree rate, but the primary education rate did not change between hearing and physically person with disability. Physical activity participation differed between hearing and physically person with disability, and it was observed that hearing-impaired people participated in more physical activities. The level of well-being of the physically disabled was significantly better than the hearing impaired. As conclusion, it is observed that the people with the lowest motivation to participate in sports are primary school graduates and those with high welfare have a high motivation to participate in sports.

Energy expenditure, oxygen consumption, and heart rate while exercising on seven different indoor cardio machines at maximum and self-selected submaximal intensity.

Objective: One of the main objectives of practicing indoor cardiovascular exercise is to maximize caloric expenditure. This study aimed to compare energy expenditure (EE), oxygen consumption (VO2), and heart rate (HR) recorded in middle-aged adults while exercising on seven different indoor cardiovascular machines at self-selected maximal and submaximal intensity. Method: Thirty recreational-active adult males (Age: 41.69 ± 4.64) performed 12-min bouts at RPE (Rate of perceived exertion) 17 and maximum intensity (MAX INT) on the following indoor cardio machines: Recumbent bike (r_BIKE), upright bike (u-BIKE), spin bike (s-BIKE), rowing machine (ROW), elliptical trainer (ELLIP), stair climber (STAIR), and treadmill (TMILL). Heart rate (HR) and oxygen consumption (VO2) were measured during exercise, whereas EE (energy expenditure) was calculated indirectly. Results: Overall, TMILL induced the highest levels of EE, VO2, and HR, followed by STAIR, ELLIP, s_BIKE, u_BIKE, ROW, and r_BIKE. RPE was reliable across exercise modalities (r_BIKE, u-BIKE, s-BIKE, ROW, ELLIP, STAIR, and TMILL) and intensities (RPE 17 and MAX INT) for EE, HR, and VO2 measurements. Conclusion: To maximize EE while performing indoor cardiovascular exercise for recreational active middle-aged male participants, the TMILL is the best option, followed by the STAIR and the ELLIP. The least recommended options are, respectively, s_BIKE, u_BIKE, ROW, and r_BIKE. Beyond caloric expenditure considerations, promoting exercises that participants genuinely enjoy can enhance adherence, fostering sustained health benefits. Furthermore, RPE is a reliable tool for assessing EE, VO2, and HR across different exercise modalities and intensities.

Effects of Swedish Massage at Different Times of the Day on Dynamic and Static Balance in Taekwondo Athletes.

The purpose of this study is to investigate the impact of different durations of Swedish massage on the static and dynamic balance at different times of the day in taekwondo athletes. Twelve taekwondo athletes who had been practicing on a regular basis for more than 5 years participated in this study. Taekwondo athletes completed static and dynamic balance tests either after a no-massage protocol (NMP), a five-minute massage protocol (5MMP), a ten-minute massage protocol (10MMP), or a fifteen-minute massage protocol (15MMP) two times a day in the morning (08:00-12:00) and in the evening (16:00-20:00), on non-consecutive days. The findings of this study suggest that the duration of the massage has a discernible impact on dynamic balance, particularly with regard to the right foot. Taekwondo athletes who received a 10MMP or 15MMP displayed significantly improved dynamic balance compared to those in the NMP. Importantly, these improvements were independent of the time of day when the massages were administered. It underscores the potential benefits of incorporating short-duration Swedish massages into taekwondo athletes' pre-competition routines to enhance dynamic balance. These findings highlight the potential benefits of incorporating short-duration Swedish massages into taekwondo athletes' pre-competition routines to enhance dynamic balance, a critical component of their performance, regardless of the time of day.

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.

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.

Does body mass index distinguish motor proficiency, social and emotional maturity among adolescent girls?

BACKGROUND: The 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. METHODS: 140 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. RESULTS: normal-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. CONCLUSIONS: The present study demonstrates that BMI has a direct influence on adolescents' gross motor proficiency and social maturity.

Effects of combined aerobic exercise and diet on cardiometabolic health in patients with obesity and type 2 diabetes: a systematic review and meta-analysis.

BACKGROUND: Lifestyle modifications involving diet and exercise are recommended for patients diagnosed with obesity and type 2 diabetes mellitus (T2DM). The purpose of this review was to systematically evaluate the effects of combined aerobic exercise and diet (AEDT) on various cardiometabolic health-related indicators among individuals with obesity and T2DM. METHODOLOGY: A comprehensive search of the PubMed/Medline, Web of Science, Scopus, Science Direct, Cochrane, and Google Scholar databases was conducted for this meta-analysis. The Cochrane risk of bias tool was used to evaluate eligible studies, and the GRADE tool was used to rate the certainty of evidence. A random-effects model for continuous variables was used, and the results were presented as mean differences or standardised mean differences with 95% confidence intervals. RESULTS: A total of 16,129 studies were retrieved; 20 studies were included, and data were extracted from 1,192 participants. The findings revealed significant improvements in body mass index, body weight, waist circumference, systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides, fasting blood glucose, fasting plasma insulin, glycated hemoglobin, leptin, interleukin-6, C-reactive protein, and adiponectin (p < 0.05) compared to the standard treatment (ST) group. No significant differences were observed between the AEDT and ST groups in fat mass, hip circumference, waist-to-hip ratio, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and tumor necrosis factor-alpha. The present findings are based on low- to moderate-quality evidence. CONCLUSIONS: AEDT may be a critical behavior for holistic cardiometabolic health-related benefits as a contemporary anti-obesity medication due to its significant positive impact on patients with obesity and T2DM. Nevertheless, further robust evidence is necessary to determine whether AEDT is an effective intervention for lowering cardiovascular and metabolic risk factors among individuals with obesity and T2DM.

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.

Are there differences between Mediterranean diet and the consumption of harmful substances on quality of life?-an explanatory model in secondary education regarding gender.

Background: Adolescence 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. Methods: A 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). Results: The 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 (ß = 0.904; p ≤ 0.001). Conclusion: In 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.

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.

Impacts of different triathlon races on systemic cytokine profile and metabolic parameters in healthy individuals: a systematic review.

The present systematic review aimed to discuss the impacts of different triathlon protocols on the level of pro and anti-inflammatory cytokines, as well as biomarkers related to the performance of healthy individuals. Four databases [PubMed (28 articles), Scopus (24 articles), Science Direct (200 articles), and SPORT Discus (1101 articles) were assessed. The eligibility criteria were applied, and the selected articles were used in the peer review, independently, as they were identified by March 2022. Of the 1359 articles found, 10 were included in this systematic review. Despite the difference in triathlon protocols, it was observed an increase in pro and anti-inflammatory cytokines including IL-4 and IL-10, and chemokines, such as IL-8 and MCP-1. Moreover, the anti-inflammatory serum levels increase after triathlon. Overall, the studies also reported enhancement in the serum levels of cortisol, creatine kinase, C reactive protein, Endothelial Growth Factor, Vascular Endothelial Growth Factor, Myostatin, Lactate dehydrogenase, free fatty acids, and lactate dehydrogenase in triathlon athletes. This systematic review indicates that different triathlon race promotes an acute elevation of circulating cytokines and chemokines levels which return to standard levels after triathlon races. The findings of this systematic review demonstrate that the modulation of inflammatory parameters may be associated with an increase in metabolic indicators (CK, Cortisol, and LDH) after the end of different types of triathlon races.