Effects of 700 and 3500 MHz 5G radiofrequency exposure on developing zebrafish embryos.

Telecommunications industries are rapidly deploying the fifth generation (5G) spectrum and there is public concern about the safety and health impacts of this type of Radio Frequency Radiation (RFR), in part because of the lack of comparable scientific evidence. In this study we have used a validated commercially available setting producing a uniform field to expose zebrafish embryos (ZFe) to unmodulated 700 and 3500 MHz frequencies. We have combined a battery of toxicity, developmental and behavioral assays to further explore potential RFR effects. Our neurobehavioral profiles include a tail coiling assay, a light/dark activity assay, two thigmotaxis anxiety assays (auditory and visual stimuli), and a startle response - habituation assay in response to auditory stimuli. ZFe were exposed for 1 and 4 h during the blastula period of development and endpoints evaluated up to 120 hours post fertilization (hpf). Our results show no effects on mortality, hatching or body length. However, we have demonstrated specific organ morphological effects, and behavioral effects in activity, anxiety-like behavior, and habituation that lasted in larvae exposed during the early embryonic period. A decrease in acetylcholinesterase activity was also observed and could explain some of the observed behavioral alterations. Interestingly, effects were more pronounced in ZFe exposed to the 700 MHz frequency, and especially for the 4 h exposure period. In addition, we have demonstrated that our exposure setup is robust, flexible with regard to frequency and power testing, and highly comparable. Future work will include exposure of ZFe to 5G modulated signals for different time periods to better understand the potential health effects of novel 5G RFR.

Pinning Control for the p53-Mdm2 Network Dynamics Regulated by p14ARF.

p53 regulates the cellular response to genotoxic damage and prevents carcinogenic events. Theoretical and experimental studies state that the p53-Mdm2 network constitutes the core module of regulatory interactions activated by cellular stress induced by a variety of signaling pathways. In this paper, a strategy to control the p53-Mdm2 network regulated by p14ARF is developed, based on the pinning control technique, which consists into applying local feedback controllers to a small number of nodes (pinned ones) in the network. Pinned nodes are selected on the basis of their importance level in a topological hierarchy, their degree of connectivity within the network, and the biological role they perform. In this paper, two cases are considered. For the first case, the oscillatory pattern under gamma-radiation is recovered; afterward, as the second case, increased expression of p53 level is taken into account. For both cases, the control law is applied to p14ARF (pinned node based on a virtual leader methodology), and overexpressed Mdm2-mediated p53 degradation condition is considered as carcinogenic initial behavior. The approach in this paper uses a computational algorithm, which opens an alternative path to understand the cellular responses to stress, doing it possible to model and control the gene regulatory network dynamics in two different biological contexts. As the main result of the proposed control technique, the two mentioned desired behaviors are obtained.

Trajectory Tracking on Uncertain Complex Networks via NN-Based Inverse Optimal Pinning Control.

A new approach for trajectory tracking on uncertain complex networks is proposed. To achieve this goal, a neural controller is applied to a small fraction of nodes (pinned ones). Such controller is composed of an on-line identifier based on a recurrent high-order neural network, and an inverse optimal controller to track the desired trajectory; a complete stability analysis is also included. In order to verify the applicability and good performance of the proposed control scheme, a representative example is simulated, which consists of a complex network with each node described by a chaotic Lorenz oscillator.

[Degree of control of cardiovascular risk factors among a patient population with diabetes mellitus type 1 and 2]. / Grado de control de factores de riesgo cardiovascular en una población de pacientes con diabetes mellitus tipo 1 y 2 de difícil manejo.

OBJECTIVE: To evaluate the degree of control of metabolic goals in a group of very selected type 1 and 2 diabetic patients. METHODS: A cross-sectional and descriptive study was done. Patients were enrolled consecutively in the Diabetes Clinic in a tertiary-care hospital in México City during the period between april and july 2005. The population at this clinic is very selected as demonstrated by the fact that all type 2 diabetic patients were treated with drugs for diabetic control, including insulin in 43% of them. We used the goals recommended by the American Diabetes Association (ADA) as parameters to analyze and additionally included non-HDL cholesterol and the atherogenic index. RESULTS: A total of 530 patients were included; 468 (58.8% female) had type 2 diabetes, with an average age of 58.5 years; 62 (65% female) patients had type 1 diabetes, with an average age of 31.2 years. The mean HbA1c values were 10.2 +/- 2.8 and 9.0 +/- 2.4 in type 1 and type 2 diabetic patients, respectively. The proportion of diabetic type 1 and 2 patients reaching treatment goals were 12.9% and 23.7% for HbA1c (p=0.02), 82.2% and 57.2% for both systolic and diastolic blood pressure (p=0.0001), 75.8% and 49.3% for triglycerides (p=0.0001), 45.1% and 35.6% for LDL-c (p=0.16), 51.6% and 53.4% for HDL-c (p=0.79), 56.4% and 43.3% for non-HDL cholesterol (p=0.03) and 58.0% and 55.1% for atherogenic index (p=0.66), respectively. The proportion of patients reaching all the optimal treatment goals (non-HDL cholesterol, HbA1c, arterial blood pressure and triglycerides) was 6.4% for type 1 diabetic patients and 4.4% for type 2 patients (p=0.6). Factors associated with achieving goal values in a multiple regression analysis were drug treatment for high blood pressure, use of lipid lowering drugs, insulin use and a history of stroke. CONCLUSIONS: Our results are not comparable with other publications of series evaluating the same parameters in open populations. The results show that the degree of control of evaluated risk factors is not good, principally in the case of glucemic control; it is necessary to plan strategies that help to reach these goals in diabetic patients.

Grado de control de factores de riesgo cardiovascular en una población de pacientes con diabetes mellitus tipo 1 y 2 de difícil manejo / Degree of control of cardiovascular risk factors among a patient population with diabetes mellitus type 1 and 2

Objetivo: Evaluar la medida en que se alcanzan las metas de control en un grupo de pacientes seleccionados con diabetes tipo 1 o 2. Métodos: Estudio transversal y descriptivo en el que se incluyeron pacientes atendidos de manera consecutiva en la Consulta Externa de Diabetes del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán entre abril y julio de 2005; la población seleccionada era de difícil control metabólico; todos los pacientes con diabetes mellitus tipo 2 recibían fármacos para controlar la glucemia, incluyendo insulina en 43% de ellos. Analizamos los criterios de control recomendados por la Asociación Americana de Diabetes, las cifras de colesterol no-HDL y el índice aterogénico. Resultados: Se incluyeron 530 pacientes: 468 (mujeres 58.8%) tenían diabetes tipo 2, con edad promedio de 58.5 años, y 62 (mujeres 65%) tenían diabetes tipo 1, con edad promedio de 31.2 años. El valor promedio de HbA1c fue de 10.2±2.8 y 9.0±2.4 para diabéticos tipo 1 y tipo 2. Las proporciones de pacientes con diabetes tipos 1 y 2 dentro de las metas de tratamiento fueron de 12.9 y 23.7% para HbA1c (p=0.02), 82.2 y 57.2% para presión arterial sistólica y diastólica (p=0.0001), 75.8 y 49.3% para triglicéridos (p=0.0001), 45.1 y 35.6% para c-LDL (p=0.16), 51.6 y 53.4% para c-HDL (p= 0.79), 56.4 y 43.3% para c-no-HDL (p=0.03) y 58.0% versus 55.1% para índice aterogénico (p=0.66). La proporción de pacientes que alcanzaron metas de colesterol no-HDL, HbA1c, presión arterial y triglicéridos fue de 6.4% para diabéticos tipo 1 y de 4.4% para diabéticos tipo 2 (p=0.6). En el análisis de regresión logística múltiple, las variables asociadas con mayor probabilidad de alcanzar las metas de control fueron el tratamiento con antihipertensivos, el uso de hipolipemiantes, de insulina y el antecedente de enfermedad vascular cerebral. Conclusiones: Si bien nuestros resultados no pueden ser directamente comparables con los publicados en otras series en poblaciones abiertas, muestran que el grado de control de los factores de riesgo considerados no es aceptable, sobre todo en los niveles de glucemia; es necesario implementar programas que ayuden a cumplir estas metas en pacientes diabéticos.

OBJECTIVE: To evaluate the degree of control of metabolic goals in a group of very selected type 1 and 2 diabetic patients. METHODS: A cross-sectional and descriptive study was done. Patients were enrolled consecutively in the Diabetes Clinic in a tertiary-care hospital in México City during the period between april and july 2005. The population at this clinic is very selected as demonstrated by the fact that all type 2 diabetic patients were treated with drugs for diabetic control, including insulin in 43% of them. We used the goals recommended by the American Diabetes Association (ADA) as parameters to analyze and additionally included non-HDL cholesterol and the atherogenic index. RESULTS: A total of 530 patients were included; 468 (58.8% female) had type 2 diabetes, with an average age of 58.5 years; 62 (65% female) patients had type 1 diabetes, with an average age of 31.2 years. The mean HbA1c values were 10.2 +/- 2.8 and 9.0 +/- 2.4 in type 1 and type 2 diabetic patients, respectively. The proportion of diabetic type 1 and 2 patients reaching treatment goals were 12.9% and 23.7% for HbA1c (p=0.02), 82.2% and 57.2% for both systolic and diastolic blood pressure (p=0.0001), 75.8% and 49.3% for triglycerides (p=0.0001), 45.1% and 35.6% for LDL-c (p=0.16), 51.6% and 53.4% for HDL-c (p=0.79), 56.4% and 43.3% for non-HDL cholesterol (p=0.03) and 58.0% and 55.1% for atherogenic index (p=0.66), respectively. The proportion of patients reaching all the optimal treatment goals (non-HDL cholesterol, HbA1c, arterial blood pressure and triglycerides) was 6.4% for type 1 diabetic patients and 4.4% for type 2 patients (p=0.6). Factors associated with achieving goal values in a multiple regression analysis were drug treatment for high blood pressure, use of lipid lowering drugs, insulin use and a history of stroke. CONCLUSIONS: Our results are not comparable with other publications of series evaluating the same parameters in open populations. The results show that the degree of control of evaluated risk factors is not good, principally in the case of glucemic control; it is necessary to plan strategies that help to reach these goals in diabetic patients.