The Gini coefficient is a statistical tool generally used by economists to quantify income inequality. However, it can be applied to any kind of data with unequal distribution, including heart rate variability (HRV). To assess the application of the Gini coefficient to measure inequality in power spectral density of RR intervals, and to use this application as a psychophysiological indicator of mental stress. Thirteen healthy subjects (19 ± 1.5 years) participated in this study, and their RR intervals were obtained by electrocardiogram during rest (five minutes) and during mental stress (arithmetic challenge; five minutes). These RR intervals were used to obtain the estimates of power spectral densities (PSD). The limits for the PSD bands were defined from 0.15 to 0.40 Hz for high frequency band (HF), from 0.04 to 0.15 Hz for low frequency band (LF), from 0.04 to 0.085 Hz for first low frequency sub-band (LF1) and from 0.085 to 0.15 Hz for second low frequency sub-band (LF2). The spectral Gini coefficient (SpG) was proposed to measure the inequality in the power distribution of the RR intervals in each of above-mentioned HRV bands. SpG from each band was compared with its respective traditional index of HRV during the conditions of rest and mental stress. All the differences were considered statistically significant for p < 0.05. There was a significant decrease in HF power (p = 0.046), as well as significant increases in heart rate (p = 0.004), LF power (p = 0.033), LF2 power (p = 0.019) and LF/HF (p = 0.002) during mental stress. There was also a significant increase in SpG(LF) (p = 0.009) and SpG(LF2) (p = 0.033) during mental stress. Coefficient of variation showed SpG has more homogeneity compared to the traditional index of HRV during mental stress. This pilot study suggested that spectral inequality of Heart Rate Variability analyzed using the Gini coefficient seems to be an independent and homogeneous psychophysiological indicator of mental stress. Also, HR, LF/HF, SpG(LF) of HRV are possibly important, reliable and valid indicators of mental stress.

Acute myeloid leukemia (AML) is a diverse malignancy originating from myeloid progenitor cells, with significant genetic and clinical variability. Modern classification systems like those from the World Health Organization (WHO) and European LeukemiaNet use immunophenotyping, molecular genetics, and clinical features to categorize AML subtypes. This classification highlights crucial genetic markers such as FLT3, NPM1 mutations, and MLL-AF9 fusion, which are essential for prognosis and directing targeted therapies. The MLL-AF9 fusion protein is often linked with therapy-resistant AML, highlighting the risk of relapse due to standard chemotherapeutic regimes. In this sense, factors like the ZEB, SNAI, and TWIST gene families, known for their roles in epithelial–mesenchymal transition (EMT) and cancer metastasis, also regulate hematopoiesis and may serve as effective therapeutic targets in AML. These genes contribute to cell proliferation, differentiation, and extramedullary hematopoiesis, suggesting new possibilities for treatment. Advancing our understanding of the molecular mechanisms that promote AML, especially how the bone marrow microenvironment affects invasion and drug resistance, is crucial. This comprehensive insight into the molecular and environmental interactions in AML emphasizes the need for ongoing research and more effective treatments.

Introduction: Gini coefficient (Gini index or Gini ratio) is a parameter that is normally used in economy to measure the income distribution in a country or in the whole wide world, but it can be used to measure any kind of distribution. In the present study it is exposed an innovative proposal of application of the Gini coefficient to Heart Rate Variability (HRV) like a psychophysiological indicator of mental stress.Objective: To assess the application of the Gini coefficient as a psychophysiological indicator of mental stress.Methods: A non-observational crossover study, carried out in the biomedical laboratory of the Medical University of Santiago de Cuba. The involved participants are 13 healthy individuals (age 19 ± 1.5 years). Heart rate was continuously recorded at rest (5 minutes) and during a mental stress (5 minutes). Linear and nonlinear methods of heart rate variability were assessed, and 2 new indicators (Sequential and Non-Sequential Gini) were calculated and proposed to measure HRV differences between states.Results: When comparing rest and mental stress conditions, a sensible decrease of the traditional indicators of the HRV was founded (p<0,05), an increase of the heart rate (p=0,004) and of the Sequential Gini (p=0,004) and Non-Sequential Gini (p=0,04).Conclusions: The results suggest that temporary inequality of the RR intervals analyzed from the Gini coefficient could be an adequate indicator of sympathetic activity present during the mental stress. Introducción: El coeficiente de Gini o índice de Gini es un parámetro que normalmente se usa en economía para medir la distribución del ingreso en un país o en todo el mundo, pero puede usarse para medir cualquier tipo de distribución. En el presente estudio se expone una propuesta innovadora de aplicación del coeficiente de Gini a la Variabilidad de la Frecuencia Cardíaca (VFC) como indicador psicofisiológico del estrés mental.Objetivo: Evaluar la aplicación del coeficiente de Gini como indicador psicofisiológico de estrés mental.Métodos: Estudio no observacional de corte transversal, llevado a cabo en el laboratorio de estudios biomédicos de la Universidad Médica de Santiago Cuba. Los participantes involucrados fueron 13 individuos sanos (edad 19 ± 1,5 años). La frecuencia cardíaca se registró continuamente en reposo (5 minutos) y durante un estrés mental (5 minutos). Se evaluaron métodos lineales y no lineales de variabilidad de la frecuencia cardíaca, y se calcularon y propusieron 2 nuevos indicadores (Gini secuencial y no secuencial) para medir las diferencias de VFC entre estados.Resultados: Al comparar las condiciones de reposo y estrés mental, se encontró una sensible disminución de los indicadores tradicionales de la VFC (p<0,05), un aumento de la frecuencia cardiaca (p=0,004) y del Gini Secuencial (p=0,004) y Gini no secuencial (p=0,04).Conclusiones: Los resultados sugieren que la desigualdad temporal de los intervalos RR analizados a partir del coeficiente de Gini podría ser un indicador adecuado de la actividad simpática presente durante el estrés mental.

Background: Predicting beat-to-beat blood pressure has several clinical applications. While most machine learning models focus on accuracy, it is necessary to build models that explain the relationships of hemodynamical parameters with blood pressure without sacrificing accuracy, especially during exercise. Objective: The aim of this study is to use the RuleFit model to measure the importance, interactions, and relationships among several parameters extracted from photoplethysmography (PPG) and electrocardiography (ECG) signals during a dynamic weight-bearing test (WBT) and to assess the accuracy and interpretability of the model results. Methods: RuleFit was applied to hemodynamical ECG and PPG parameters during rest and WBT in six healthy young subjects. The WBT involves holding a 500 g weight in the left hand for 2 min. Blood pressure is taken in the opposite arm before and during exercise thereof. Results: The root mean square error of the model residuals was 4.72 and 2.68 mmHg for systolic blood pressure and diastolic blood pressure, respectively, during rest and 4.59 and 4.01 mmHg, respectively, during the WBT. Furthermore, the blood pressure measurements appeared to be nonlinear, and interaction effects were observed. Moreover, blood pressure predictions based on PPG parameters showed a strong correlation with individual characteristics and responses to exercise. Conclusion: The RuleFit model is an excellent tool to study interactions among variables for predicting blood pressure. Compared to other models, the RuleFit model showed superior performance. RuleFit can be used for predicting and interpreting relationships among predictors extracted from PPG and ECG signals.

Acute Myeloid Leukemia (AML) is a diverse malignancy originating from myeloid progenitor cells, with significant genetic and clinical variability. Modern classification systems like those from the World Health Organization (WHO) and European LeukemiaNet use immunophenotyping, molecular genetics, and clinical features to categorize AML subtypes. This classification highlights crucial genetic markers such as FLT3, NPM1 mutations, and the MLL-AF9 fusion, which are essential for prognosis and directing targeted therapies. The MLL-AF9 fusion protein is often linked with therapy-resistant AML due, highlighting the risk of relapse due standard chemotherapeutic regimes. In this sense, factors like the ZEB, SNAI, and TWIST gene families, known for their roles in epithelial-mesenchymal transition (EMT) and cancer metastasis, also regulate hematopoiesis and may serve as effective therapeutic targets in AML. These genes contribute to cell proliferation, differentiation, and extramedullary hematopoiesis, suggesting new possibilities for treatment. Advancing our understanding of the molecular mechanisms that promote AML, especially how the bone marrow microenvironment affects invasion and drug resistance, is crucial. This comprehensive insight into the molecular and environmental interactions in AML emphasizes the need for ongoing research and more effective treatments.