Search Results (17)

Myocardial infarction (MI) remains the leading cause of death globally, imposing a significant burden on healthcare systems and patients. The gut–heart axis, a bidirectional network connecting gut health to cardiovascular outcomes, has recently emerged as a critical factor in MI pathophysiology. Disruptions in this axis, including gut dysbiosis and compromised intestinal barrier integrity, lead to systemic inflammation driven by gut-derived metabolites like lipopolysaccharides (LPSs) and trimethylamine N-oxide (TMAO), both of which exacerbate MI progression. In contrast, metabolites such as short-chain fatty acids (SCFAs) from a balanced microbiota exhibit protective effects against cardiac damage. This review examines the molecular mediators of the gut–heart axis, considering the role of factors like sex-specific hormones, aging, diet, physical activity, and alcohol consumption on gut health and MI outcomes. Additionally, we highlight therapeutic approaches, including dietary interventions, personalized probiotics, and exercise regimens. Addressing the gut–heart axis holds promise for reducing MI risk and improving recovery, positioning it as a novel target in cardiovascular therapy. Full article

Honey is a natural sweet element that bees make with flower nectar, revered for its distinct flavor, nutritional value, and potential health benefits. Chilean beekeeping has a diverse range of honey varieties, many of which are unique. The quillay (Quillaja saponaria Molina, soapbark tree) is a Chilean endemic tree whose honey has not been studied in depth. We characterized various Chilean honeys with different botanical origins, with a particular focus on quillay tree honey, analyzing its total phenolic and flavonoid content and its antioxidant activities. Cytotoxicity and hepatoprotective activity were also evaluated using HuH-7 cells. The Spearman correlation between the percentage of quillay pollen in the honey samples and the total phenolic content (R = 0.72; p < 0.05), plus the oxygen radical absorbance capacity, suggests that compounds from quillay contribute to the overall antioxidant capacity of honey. Unifloral quillay honey extracts also protect hepatic cells from oxidative damage induced by peroxyl radicals generated by AAPH. This analysis sheds light on the potential of quillay tree honey, underscoring its significance as a natural source of bioactive phenolic compounds with possible hepatoprotective effects. Full article

Background/Objectives: Functional MRI (fMRI) is widely used to assess language lateralization, but its application in patients with brain tumors can be hindered by cognitive impairments, compensatory neuroplasticity, and artifacts due to patient movement or severe aphasia. Gray matter volume (GMV) analysis via voxel-based morphometry (VBM) in language-related brain regions may offer a stable complementary approach. This study investigates the relationship between GMV and fMRI-derived language lateralization in healthy individuals and patients with left-hemisphere brain tumors, aiming to enhance accuracy in complex cases. Methods: The MRI data from 22 healthy participants and 28 individuals with left-hemisphere brain tumors were analyzed. Structural T1-weighted and functional images were obtained during three language tasks. Language lateralization was assessed based on activation in predefined regions of interest (ROIs), categorized as typical (left) or atypical (right or bilateral). The GMV in these ROIs was measured using VBM. Linear regressions explored GMV-lateralization associations, and logistic regressions predicted the lateralization based on the GMV. Results: In the healthy participants, typical left-hemispheric language dominance correlated with higher GMV in the left pars opercularis of the inferior frontal gyrus. The brain tumor participants with atypical lateralization showed increased GMV in six right-hemisphere ROIs. The GMV in the language ROIs predicted the fMRI language lateralization, with AUCs from 80.1% to 94.2% in the healthy participants and 78.3% to 92.6% in the tumor patients. Conclusions: GMV analysis in language-related ROIs effectively complements fMRI for assessing language dominance, particularly when fMRI is challenging. It correlates with language lateralization in both healthy individuals and brain tumor patients, highlighting its potential in preoperative language mapping. Further research with larger samples is needed to refine its clinical utility. Full article

The COVID-19 pandemic presents unique requirements for accessible, reliable testing, and many testing platforms and sampling techniques have been developed over the course of the pandemic. Not all test methods have been systematically compared to each other or a common gold standard, and the performance of tests developed in the early epidemic have not been consistently re-evaluated in the context of new variants. We conducted a repeated measures study with adult healthcare workers presenting for SARS-CoV-2 testing. Participants were tested using seven testing modalities. Test sensitivity was compared using any positive PCR test as the gold standard. A total of 325 individuals participated in the study. PCR tests were the most sensitive (saliva PCR 0.957 ± 0.048, nasopharyngeal PCR 0.877 ± 0.075, oropharyngeal PCR 0.849 ± 0.082). Standard nasal rapid antigen tests were less sensitive but roughly equivalent (BinaxNOW 0.613 ± 0.110, iHealth 0.627 ± 0.109). Oropharyngeal rapid antigen tests were the least sensitive (BinaxNOW 0.400 ± 0.111, iHealth brands 0.311 ± 0.105). PCR remains the most sensitive testing modality for the diagnosis of COVID-19 and saliva PCR is significantly more sensitive than oropharyngeal PCR and equivalent to nasopharyngeal PCR. Nasal AgRDTs are less sensitive than PCR but have benefits in convenience and accessibility. Saliva-based PCR testing is a viable alternative to traditional swab-based PCR testing for the diagnosis of COVID-19. Full article

There is a significant risk of injury in sports and intense competition due to the demanding physical and psychological requirements. Hamstring strain injuries (HSIs) are the most prevalent type of injury among professional soccer players and are the leading cause of missed days in the sport. These injuries stem from a combination of factors, making it challenging to pinpoint the most crucial risk factors and their interactions, let alone find effective prevention strategies. Recently, there has been growing recognition of the potential of tools provided by artificial intelligence (AI). However, current studies primarily concentrate on enhancing the performance of complex machine learning models, often overlooking their explanatory capabilities. Consequently, medical teams have difficulty interpreting these models and are hesitant to trust them fully. In light of this, there is an increasing need for advanced injury detection and prediction models that can aid doctors in diagnosing or detecting injuries earlier and with greater accuracy. Accordingly, this study aims to identify the biomarkers of muscle injuries in professional soccer players through biomechanical analysis, employing several ML algorithms such as decision tree (DT) methods, discriminant methods, logistic regression, naive Bayes, support vector machine (SVM), K-nearest neighbor (KNN), ensemble methods, boosted and bagged trees, artificial neural networks (ANNs), and XGBoost. In particular, XGBoost is also used to obtain the most important features. The findings highlight that the variables that most effectively differentiate the groups and could serve as reliable predictors for injury prevention are the maximum muscle strength of the hamstrings and the stiffness of the same muscle. With regard to the 35 techniques employed, a precision of up to 78% was achieved with XGBoost, indicating that by considering scientific evidence, suggestions based on various data sources, and expert opinions, it is possible to attain good precision, thus enhancing the reliability of the results for doctors and trainers. Furthermore, the obtained results strongly align with the existing literature, although further specific studies about this sport are necessary to draw a definitive conclusion. Full article

Exercise increases the cost of breathing (COB) due to increased lung ventilation ($\dot{\mathrm{V}}$E), inducing respiratory muscles deoxygenation ($\nabla$SmO₂), while the increase in workload implies $\nabla$SmO₂ in locomotor muscles. This phenomenon has been proposed as a leading cause of exercise intolerance, especially in clinical contexts. The use of high-flow nasal cannula (HFNC) during exercise routines in rehabilitation programs has gained significant interest because it is proposed as a therapeutic intervention for reducing symptoms associated with exercise intolerance, such as fatigue and dyspnea, assuming that HFNC could reduce exercise-induced $\nabla$SmO₂. SmO₂ can be detected using optical wearable devices provided by near-infrared spectroscopy (NIRS) technology, which measures the changes in the amount of oxygen bound to chromophores (e.g., hemoglobin, myoglobin, cytochrome oxidase) at the target tissue level. We tested in a study with a cross-over design whether the muscular desaturation of m.vastus lateralis and m.intercostales during a high-intensity constant-load exercise can be reduced when it was supported with HFNC in non-physically active adults. Eighteen participants (nine women; age: 22 ± 2 years, weight: 65.1 ± 11.2 kg, height: 173.0 ± 5.8 cm, BMI: 21.6 ± 2.8 kg·m⁻²) were evaluated in a cycle ergometer (15 min, 70% maximum watts achieved in ergospirometry ($\dot{\mathrm{V}}$O₂-peak)) breathing spontaneously (control, CTRL) or with HFNC support (HFNC; 50 L·min⁻¹, fiO₂: 21%, 30 °C), separated by seven days in randomized order. Two-way ANOVA tests analyzed the $\nabla$SmO₂ (m.intercostales and m.vastus lateralis), and changes in $\dot{\mathrm{V}}$E and $\nabla$SmO₂·$\dot{\mathrm{V}}$E⁻¹. Dyspnea, leg fatigue, and effort level (RPE) were compared between trials by the Wilcoxon matched-paired signed rank test. We found that the interaction of factors (trial × exercise-time) was significant in $\nabla$SmO₂-m.intercostales, $\dot{\mathrm{V}}$E, and ($\nabla$SmO₂-m.intercostales)/$\dot{\mathrm{V}}$E (p < 0.05, all) but not in $\nabla$SmO₂-m.vastus lateralis. $\nabla$SmO₂-m.intercostales was more pronounced in CTRL during exercise since 5′ (p < 0.05). Hyperventilation was higher in CTRL since 10′ (p < 0.05). The $\nabla$SmO₂·$\dot{\mathrm{V}}$E⁻¹ decreased during exercise, being lowest in CTRL since 5′. Lower dyspnea was reported in HFNC, with no differences in leg fatigue and RPE. We concluded that wearable optical biosensors documented the beneficial effect of HFNC in COB due to lower respiratory $\nabla$SmO₂ induced by exercise. We suggest incorporating NIRS devices in rehabilitation programs to monitor physiological changes that can support the clinical impact of the therapeutic intervention implemented. Full article

Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant time series behavior is not regular and is affected by several environmental and urban factors. In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM${}_{2.5}$ pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with a set of multilayer perceptron to forecast extreme values of PM${}_{2.5}$ for each cluster. The proposed model was applied to analyze five-year pollution data obtained from nine weather stations in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves performance metrics when forecasting daily extreme values of PM${}_{2.5}$. Full article

Long-term dependence is an essential feature for the predictability of time series. Estimating the parameter that describes long memory is essential to describing the behavior of time series models. However, most long memory estimation methods assume that this parameter has a constant value throughout the time series, and do not consider that the parameter may change over time. In this work, we propose an automated methodology that combines the estimation methodologies of the fractional differentiation parameter (and/or Hurst parameter) with its application to Recurrent Neural Networks (RNNs) in order for said networks to learn and predict long memory dependencies from information obtained in nonlinear time series. The proposal combines three methods that allow for better approximation in the prediction of the values of the parameters for each one of the windows obtained, using Recurrent Neural Networks as an adaptive method to learn and predict the dependencies of long memory in Time Series. For the RNNs, we have evaluated four different architectures: the Simple RNN, LSTM, the BiLSTM, and the GRU. These models are built from blocks with gates controlling the cell state and memory. We have evaluated the proposed approach using both synthetic and real-world data sets. We have simulated ARFIMA models for the synthetic data to generate several time series by varying the fractional differentiation parameter. We have evaluated the proposed approach using synthetic and real datasets using Whittle’s estimates of the Hurst parameter classically obtained in each window. We have simulated ARFIMA models in such a way that the synthetic data generate several time series by varying the fractional differentiation parameter. The real-world IPSA stock option index and Tree Ringtime series datasets were evaluated. All of the results show that the proposed approach can predict the Hurst exponent with good performance by selecting the optimal window size and overlap change. Full article

This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. Methods: This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL²) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI–ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. Results: Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). Conclusion: Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics’ (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients. Full article

Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of $0.03\pm 0.013$ and an $R^2$ of $63\pm 19\%$, where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an $R^2$ up to $92\%$. The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase. Full article

The IL-1 superfamily of cytokines is a central regulator of immunity and inflammation. The family is composed of 11 cytokines (with agonist, antagonist, and anti-inflammatory properties) and 10 receptors, all tightly regulated through decoy receptor, receptor antagonists, and signaling inhibitors. Inflammation not only is an important physiological response against infection and injury but also plays a central role in atherosclerosis development. Several clinical association studies along with experimental studies have implicated the IL-1 superfamily of cytokines and its receptors in the pathogenesis of cardiovascular disease. Here, we summarize the key features of the IL-1 family, its role in immunity and disease, and how it helps shape the development of atherosclerosis. Full article

The academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative analysis with other types of analysis. This study uses a novel visual-predictive data analysis approach to obtain relevant information regarding the academic performance of students from a Peruvian university. This approach joins together domain understanding and data-visualization analysis, with the construction of machine learning models in order to provide a visual-predictive model of the students’ academic success. Specifically, a trained XGBoost Machine Learning model achieved a performance of up to 91.5% Accuracy. The results obtained alongside a visual data analysis allow us to identify the relevant variables associated with the students’ academic performances. In this study, this novel approach was found to be a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. Moreover, we were able to give some insight into the academic situation of the different careers of the university. Full article

Inflammation is a key feature of atherosclerosis. The inflammatory process is involved in all stages of disease progression, from the early formation of plaque to its instability and disruption, leading to clinical events. This strongly suggests that the use of anti-inflammatory agents might improve both atherosclerosis progression and cardiovascular outcomes. Colchicine, an alkaloid derived from the flower Colchicum autumnale, has been used for years in the treatment of inflammatory pathologies, including Gout, Mediterranean Fever, and Pericarditis. Colchicine is known to act over microtubules, inducing depolymerization, and over the NLRP3 inflammasome, which might explain its known anti-inflammatory properties. Recent evidence has shown the therapeutic potential of colchicine in the management of atherosclerosis and its complications, with limited adverse effects. In this review, we summarize the current knowledge regarding colchicine mechanisms of action and pharmacokinetics, as well as the available evidence on the use of colchicine for the treatment of coronary artery disease, covering basic, translational, and clinical studies. Full article

Experts and international organizations hypothesize that the number of cases of fatal intimate partner violence against women increased during the COVID-19 pandemic, primarily due to social distancing strategies and the implementation of lockdowns to reduce the spread of the virus. We described cases of attempted femicide and femicide in Chile before (January 2014 to February 2020) and during (March 2020 to June 2021) the pandemic. The attempted-femicide rate increased during the pandemic (incidence rate ratio: 1.22 [95% confidence interval: 1.04 to 1.43], p value: 0.016), while the rate of femicide cases remained unchanged. When a comparison between attempted-femicide and femicide cases was performed, being a foreigner, having an intimate partner relationship with a perpetrator aged 40 years or more, and the use of firearms during the assault were identified as factors associated independently with a higher probability of being a fatal victim in Chile. In conclusion, this study emphasizes that attempted femicide and femicide continued to occur frequently in family contexts both before and during the COVID-19 pandemic. Full article

Chickpeas are rich sources of bioactive compounds such as phenolic acids, flavonoids, and isoflavonoids. However, the contribution of insoluble-bound phenolics to their antioxidant properties remains unclear. Four varieties of chickpeas were evaluated for the presence of soluble (free and esterified) and insoluble-bound phenolics as well as their antiradical activity, reducing power and inhibition of peroxyl-induced cytotoxicity in human HuH-7 cells. In general, the insoluble-bound fraction showed a higher total phenolic content. Phenolic acids, flavonoids, and isoflavonoids were identified and quantified by UPLC-MS/MS. Taxifolin was identified for the first time in chickpeas. However, m-hydroxybenzoic acid, taxifolin, and biochanin A were the main phenolics found. Biochanin A was mostly found in the free fraction, while m-hydroxybenzoic acid was present mainly in the insoluble-bound form. The insoluble-bound fraction made a significant contribution to the reducing power and antiradical activity towards peroxyl radical. Furthermore, all extracts decreased the oxidative damage of human HuH-7 cells induced by peroxyl radicals, thus indicating their hepatoprotective potential. This study demonstrates that the antioxidant properties and bioactive potential of insoluble-bound phenolics of chickpeas should not be neglected. Full article