Use and outcomes of antihypertensive medication treatment in the US hypertensive population: A gender comparison.

Background: Although effective antihypertensive medications have existed for decades, only about half of the hypertensive individuals are considered to have controlled blood pressure. Limited research studies have investigated gender disparity in the utilization and effectiveness of antihypertensive medications treatment. To examine the gender difference in antihypertensive medications' use and the effect of using antihypertensive medication treatment on blood pressure control among the U.S. adult with hypertension. Methods: Analysis of National Health and Nutrition Examination Survey (NHANES) data from (1999-2012) including individuals≥18 years old with hypertension. Study variables included gender, age, race/ethnicity, obesity, smoking, comorbidities, treatment medication type, and continuity of care. We used multivariate logistic regression in STATA V14. The data is presented as adjusted odds ratios (ORs) and 95% confidence interval (CI). Results: Of the 15719 participants, 52% were female. 49% of the antihypertensive medication users had their blood pressure under control (95% CI). In the adjusted logistic regression analysis, use of antihypertensive medications was found to be 12% greater in females as compared to males (OR=1.12; CI=1.02-1.22; P<0.05). No association between gender and blood pressure control was found. Blood pressure control was less likely achieved among 50 years or younger individuals, Blacks and Hispanics, obese, and those taking calcium channel blocker (CCB). Conclusion: Hypertensive females are more likely than males to use antihypertensive medications. The effectiveness of treatment to control blood pressure is equal across males and females. Our findings have implications for practitioners to account gender-specific approaches when discussing adherence to hypertension medication treatment with their patients.

Small-fragment, high turnover: soil microenvironment fluctuation effect on tree diversity in a Neotropical montane oak forest.

Background: Soil microenvironmental variables showed an important key in α and ß-tree diversity in Neotropical montane oak forest. Thus, understanding the microenvironment fluctuation at small-fragment effects on tree diversity is crucial in maintaining the montane oak ecosystems. In this study, we hypothesized that within a relatively small-fragment (151.63 ha), tree α and ß-diversity fluctuate and specific soil microenvironmental factors could influence tree species diversity to answer three questions: Do tree α and ß-diversity differ among transects, even in a short-distance between them? Do microenvironmental variables influence tree diversity composition that occurs within a relict Neotropical montane oak forest? Is there a particular microenvironmental variable influencing tree species-specific? Methods: We established four permanent transects during a year in a relict Neotropical montane oak forest, we assessed tree diversity and specific microenvironmental variables (soil moisture, soil temperature, pH, depth litterfall and light incidence). This allowed us to evaluate how microenvironmental variables at small-fragment influence α and ß-tree diversity and tree species-specific. Results: Our results showed that α-diversity was not different among transects; however, ß-diversity of tree species was mostly explained by turnover and soil moisture, soil temperature, and light incidence were the microenvironmental variables that triggered the replacement (i.e., one species by another). Those variables also had effect on tree species-specific: Mexican beech (Fagus mexicana), Quebracho (Quercus delgadoana), Pezma (Cyathea fulva), Aguacatillo (Beilschmiedia mexicana), Pezma (Dicksonia sellowiana var. arachneosa), and Mountain magnolia (Magnolia schiedeana). Discussion: Our results confirm our hypothesis related to ß-diversity but not with α-diversity; however, the tree community structure of the diversity was similar among transects. Our study represents the first effort to evaluate and link the soil microenvironmental effect on tree α and ß-diversity, finding a high replacement in a small-fragment of Neotropical montane oak forest from eastern Mexico.

Precision recruitment for high-risk participants in a COVID-19 cohort study.

Background: Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals. Methods: We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials. Results: When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts. Conclusion: This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.

Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study.

BACKGROUND: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized. OBJECTIVE: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration. Furthermore, we aimed to determine whether ILI-associated changes in wearable sensor data differed between care-seeking and non-care-seeking populations as well as between those with confirmed influenza infection and those with ILI symptoms only. METHODS: This study comprised participants enrolled in either the FluStudy2020 or the Home Testing of Respiratory Illness (HTRI) study; both studies were similar in design and conducted between December 2019 and October 2020 in the United States. The participants self-reported ILI-related symptoms and health care-seeking behaviors via daily, biweekly, and monthly surveys. Wearable sensor data were recorded for 120 and 150 days for FluStudy2020 and HTRI, respectively. The following features were assessed: total daily steps, active time (time spent with >50 steps per minute), sleep duration, sleep efficiency, and resting heart rate. ILI-related changes in wearable sensor data were compared between the participants who sought health care and those who did not and between the participants who tested positive for influenza and those with symptoms only. Correlative analyses were performed between wearable sensor data and patient-reported outcomes. RESULTS: After combining the FluStudy2020 and HTRI data sets, the final ILI population comprised 2435 participants. Compared with healthy days (baseline), the participants with ILI exhibited significantly reduced total daily steps, active time, and sleep efficiency as well as increased sleep duration and resting heart rate. Deviations from baseline typically began before symptom onset and were greater in the participants who sought health care than in those who did not and greater in the participants who tested positive for influenza than in those with symptoms only. During an ILI event, changes in wearable sensor data consistently varied with those in patient-reported outcomes. CONCLUSIONS: Our results underscore the potential of wearable sensors to discriminate not only between individuals with and without influenza infections but also between care-seeking and non-care-seeking populations, which may have future application in health care resource planning. TRIAL REGISTRATION: Clinicaltrials.gov NCT04245800; https://clinicaltrials.gov/ct2/show/NCT04245800.

Predictors of Seeking Care for Influenza-Like Illness in a Novel Digital Study.

Background: Previous research has estimated that >50% of individuals experiencing influenza-like illness (ILI) do not seek health care. Understanding factors influencing care-seeking behavior for viral respiratory infections may help inform policies to improve access to care and protect public health. We used person-generated health data (PGHD) to identify factors associated with seeking care for ILI. Methods: Two observational studies (FluStudy2020, ISP) were conducted during the United States 2019-2020 influenza season. Participants self-reported ILI symptoms using the online Evidation platform. A log-binomial regression model was used to identify factors associated with seeking care. Results: Of 1667 participants in FluStudy2020 and 47 480 participants in ISP eligible for analysis, 518 (31.1%) and 11 426 (24.1%), respectively, sought health care. Participants were mostly female (92.2% FluStudy2020, 80.6% ISP) and aged 18-49 years (89.6% FluStudy2020, 89.8% ISP). In FluStudy2020, factors associated with seeking care included having health insurance (risk ratio [RR], 2.14; 95% CI, 1.30-3.54), more severe respiratory symptoms (RR, 1.53; 95% CI, 1.37-1.71), and comorbidities (RR, 1.37; 95% CI, 1.20-1.58). In ISP, the strongest predictor of seeking care was high symptom number (RR for 6/7 symptoms, 2.14; 95% CI, 1.93-2.38). Conclusions: Using PGHD, we confirmed low rates of health care-seeking behavior for ILI and show that having health insurance, comorbidities, and a high symptom burden were associated with seeking health care. Reducing barriers in access to care for viral respiratory infections may lead to better disease management and contribute to protecting public health.

Plastic adjustments in xylem vessel traits to drought events in three Cedrela species from Peruvian Tropical Andean forests.

Cedrela species occur within the Tropical montane cloud forest (TMCF) and rainforest in North America (Mexico), Central and South America. We assessed the hypothesis that functional xylem hydraulic architecture might be influenced by specific climatic variations. We investigated the effect of climate on tree-ring width and vessel traits (diameter, vessel density, vulnerability index and hydraulic diameter) of three relict-endemic and threatened Cedrela species (Cedrela fissilis, C. nebulosa and C. angustifolia) inhabiting Peruvian Tropical Andean cloud forests. All Cedrela species showed a significant reduction in radial growth and adjusted vessel trait linked with temperature, precipitation, and evapotranspiration. Ring-width and vessel traits showed adaptation within Cedrela species, crucial to understanding a rough indication of the plant's ability to withstand drought-induced embolism or cavitation. Our results provide evidence for hydraulic mechanisms that determine specific wood anatomical functionality to climatic variation and drought responses. Therefore, changing the frequency or intensity of future drought events might exceed the adaptive limits of TMCF tree species, resulting in a substantial reduction of hydraulic functionality in Peruvian Cedrela species.

Bioassay-Guided Fractionation of Erythrostemon yucatanensis (Greenm.) Gagnon & GP Lewis Components with Anti-hemagglutinin Binding Activity against Influenza A/H1N1 Virus.

Erythrostemon yucatanensis (Greenm.) Gagnon & GP Lewis is a legume tree native to and widely distributed in southeast Mexico, where its branches are used in traditional medicine. An in vitro evaluation of the antiviral activity of extracts and fractions from the leaves, stem bark and roots against two strains of the AH1N1 influenza virus was performed, leading to the identification of bioactive compounds in this medicinal plant. In a cytopathic effect reduction assay, the fractions from the leaves and stem bark were the active elements at the co-treatment level. These were further fractionated based on their hemagglutination inhibition activity. The analysis of spectroscopy data identified a combination of phytosterols (ß-sitosterol, stigmasterol and campesterol) in the stem bark active fraction as the main anti-hemagglutinin binding components, while 5-hydroxy-2(2-hydroxy-3,4,5-trimethoxyphenyl)-7-metoxi-4H(chromen-4-ona), which was isolated from the leaf extracts, showed a weak inhibition of viral hemagglutinin. Time of addition experiments demonstrated that the mixture of sterols had a direct effect on viral particle infectivity at the co-treatment level (IC50 = 3.125 µg/mL). This effect was also observed in the virus plaque formation inhibition assay, where the mixture showed 90% inhibition in the first 20 min of co-treatment at the same concentration. Additionally, it was found using qRT-PCR that the NP copy number was reduced by 92.85% after 60 min of co-treatment. These results are the first report of components with anti-hemagglutinin binding activity in the genus Erythrostemon sp.

The effect of climate change on Arcto-Tertiary Mexican beech forests: Exploring their past, present, and future distribution.

Fagus mexicana Martínez (Mexican beech) is an endangered Arcto-Tertiary Geoflora tree species that inhabit isolated and fragmented tropical montane cloud forests in eastern Mexico. Exploring past, present, and future climate change effects on the distribution of Mexican beech involves the study of spatial ecology and temporal patterns to develop conservation plans. These are key to understanding the niche conservatism of other forest communities with similar environmental requirements. For this study, we used species distribution models by combining occurrence records, to assess the distribution patterns and changes of the past (Last Glacial Maximum), present (1981-2010), and future (2040-2070) periods under two climate scenarios (SSP 3-7.0 & SSP 5-8.5). Next, we determined the habitat suitability and priority conservation areas of Mexican beech as associated with topography, land cover use, distance to the nearest town, and environmental variables. By considering the distribution of Mexican beech during different periods and under different climate scenarios, our study estimated that high-impact areas of Mexican beech forests were restricted to specific areas of the Sierra Madre Oriental that constitute refugia from the Last Glacial Maximum. Regrettably, our results exhibited that Mexican beech distribution has decreased 71.3% since the Last Glacial Maximum and this trend will for the next 50 years, migrating to specific refugia at higher altitudes. This suggests that the states of Hidalgo, Veracruz, and Puebla will preserve the habitat suitability features as ecological refugia, related to high moisture and north-facing slopes. For isolated and difficult-to-access areas, the proposed methods are powerful tools for relict-tree species, which deserve further conservation.

Estimating the Burden of Influenza-like Illness on Daily Activity at the Population Scale Using Commercial Wearable Sensors.

Importance: The severity of viral infections can vary widely, from asymptomatic cases to complications leading to hospitalizations and death. Milder cases, despite being more prevalent, often go undocumented, and their public health burden is not accurately estimated. Objective: To estimate the true burden of influenza-like illness (ILI) in the US population using a surrogate measure of daily steps lost as measured by commercial wearable sensors. Design, Setting, and Participants: This cohort study modeled data from 15 122 US adults who reported ILI symptoms during the 2018-2019 influenza season (before the COVID-19 pandemic) and who had a sufficient density of wearable sensor data at symptom onset. Participants' minute-level step data as measured by commercial wearable sensors were collected from October 1, 2018, through June 30, 2019. Minute-level activity time series were transformed into day-level time series per user, indicating the total number of steps daily. Main Outcomes and Measures: The primary end point was the number of steps lost during the period of 4 days before symptom onset (the latent phase) through 11 days after symptom onset (the symptomatic phase). The association between covariates and steps lost during this interval was also examined. Results: Of the 15 122 participants in this study, 13 108 (86.7%) were women, and the median age was 32 years (IQR, 27-38 years). For their ILI event, 2836 of 15 080 participants (18.8%) sought medical attention, and only 61 (0.4%) were hospitalized. Over the course of an ILI lasting 10 days, the mean cumulative loss was 4437 steps (95% CI, 4143-4731 steps). After weighting, there was an estimated overall nationwide reduction in mobility equivalent to 255.2 billion steps (95% CI, 232.9-277.6 billion steps) lost because of ILI symptoms during the study period. This finding reflects significant changes in routines, mobility, and employment and is equivalent to 15% of the active US population becoming completely immobilized for 1 day. Moreover, 60.6% of this reduction in steps (154.6 billion steps [95% CI, 138.1-171.2 billion steps]) occurred among persons who sought no medical care. Age and educational level were positively associated with steps lost. Conclusions and Relevance: These findings suggest that most of the burden of ILI in this study would have been invisible to health care and public health reporting systems. This approach has applications for public health, health care, and clinical research, from estimating costs of lost productivity at population scale, to measuring effectiveness of anti-ILI treatments, to monitoring recovery after acute viral syndromes such as during long COVID-19.

Flu@home: the Comparative Accuracy of an At-Home Influenza Rapid Diagnostic Test Using a Prepositioned Test Kit, Mobile App, Mail-in Reference Sample, and Symptom-Based Testing Trigger.

At-home testing with rapid diagnostic tests (RDTs) for respiratory viruses could facilitate early diagnosis, guide patient care, and prevent transmission. Such RDTs are best used near the onset of illness when viral load is highest and clinical action will be most impactful, which may be achieved by at-home testing. We evaluated the diagnostic accuracy of the QuickVue Influenza A+B RDT in an at-home setting. A convenience sample of 5,229 individuals who were engaged with an on-line health research platform were prospectively recruited throughout the United States. "Flu@home" test kits containing a QuickVue RDT and reference sample collection and shipping materials were prepositioned with participants at the beginning of the study. Participants responded to daily symptom surveys. If they reported experiencing cough along with aches, fever, chills, and/or sweats, they used their flu@home kit following instructions on a mobile app and indicated what lines they saw on the RDT. Of the 976 participants who met criteria to use their self-collection kit and completed study procedures, 202 (20.7%) were positive for influenza by qPCR. The RDT had a sensitivity of 28% (95% CI = 21 to 36) and specificity of 99% (98 to 99) for influenza A, and 32% (95% CI = 20 to 46) and 99% (95% CI = 98 to 99), for influenza B. Our results support the concept of app-supported, prepositioned at-home RDT kits using symptom-based triggers, although it cannot be recommended with the RDT used in this study. Further research is needed to determine ways to improve the accuracy and utility of home-based testing for influenza.

Leaf Vein Morphological Variation in Four Endangered Neotropical Magnolia Species along an Elevation Gradient in the Mexican Tropical Montane Cloud Forests.

Climatic variations influence the adaptive capacity of trees within tropical montane cloud forests species. Phenology studies have dominated current studies on tree species. Leaf vein morphology has been related to specific climatic oscillations and varies within species along altitudinal gradients. We tested that certain Neotropical broad leaf Magnolia species might be more vulnerable to leaf vein adaptation to moisture than others, as they would be more resilient to the hydric deficit. We assessed that leaf vein trait variations (vein density, primary vein size, vein length, and leaf base angle) among four Magnolia species (Magnolia nuevoleonensis, M. alejandrae, M. rzedowskiana, and Magnolia vovidesii) through the Mexican Tropical montane cloud forest with different elevation gradient and specific climatic factors. The temperature, precipitation, and potential evaporation differed significantly among Magnolia species. We detected that M. rzedowskiana and M. vovidesii with longer leaves at higher altitude sites are adapted to higher humidity conditions, and that M. nuevoleonensis and M. alejandrae inhabiting lower altitude sites are better adjusted to the hydric deficit. Our results advance efforts to identify the Magnolia species most vulnerable to climate change effects, which must focus priorities for conservation of this ecosystem, particularly in the Mexican tropical montane cloud forests.

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data.

The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic (n = 6,270, 1,265 with wearable data), COVID-19 patients reported a distinct symptom constellation that lasted longer (median of 12 versus 9 and 7 days, respectively) and peaked later after illness onset. Wearable data showed significant changes in daily steps and prevalence of anomalous resting heart rate measurements, of similar magnitudes for both the flu and COVID-19 cohorts. Our findings highlight the need to include flu comparator arms when evaluating PGHD applications aimed to be highly specific for COVID-19.

Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study.

BACKGROUND: Depression and anxiety greatly impact daily behaviors, such as sleep and activity levels. With the increasing use of activity tracking wearables among the general population, there has been a growing interest in how data collected from these devices can be used to further understand the severity and progression of mental health conditions. OBJECTIVE: This virtual 1-year observational study was designed with the objective of creating a longitudinal data set combining self-reported health outcomes, health care utilization, and quality of life data with activity tracker and app-based behavioral data for individuals with depression and anxiety. We provide an overview of the study design, report on baseline health and behavioral characteristics of the study population, and provide initial insights into how behavioral characteristics differ between groups of individuals with varying levels of disease severity. METHODS: Individuals who were existing members of an online health community (Achievement, Evidation Health Inc) and were 18 years or older who had self-reported a diagnosis of depression or anxiety were eligible to enroll in this virtual 1-year study. Participants agreed to connect wearable activity trackers that captured data related to physical activity and sleep behavior. Mental health outcomes such as the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder Questionnaire (GAD-7), mental health hospitalizations, and medication use were captured with surveys completed at baseline and months 3, 6, 9, and 12. In this analysis, we report on baseline characteristics of the sample, including mental health disease severity and health care utilization. Additionally, we explore the relationship between passively collected behavioral data and baseline mental health status and health care utilization. RESULTS: Of the 1304 participants enrolled in the study, 1277 individuals completed the baseline survey and 1068 individuals had sufficient activity tracker data. Mean age was 33 (SD 9) years, and the majority of the study population was female (77.2%, 994/1288) and identified as Caucasian (88.3%, 1137/1288). At baseline, 94.8% (1211/1277) of study participants reported experiencing depression or anxiety symptoms in the last year. This baseline analysis found that some passively tracked behavioral traits are associated with more severe forms of anxiety or depression. Individuals with depressive symptoms were less active than those with minimal depressive symptoms. Severe forms of depression were also significantly associated with inconsistent sleep patterns and more disordered sleep. CONCLUSIONS: These initial findings suggest that longitudinal behavioral and health outcomes data may be useful for developing digital measures of health for mental health symptom severity and progression.

Continuous Digital Assessment for Weight Loss Surgery Patients.

We conducted a survey about recent surgical procedures on a large connected population and requested each individual's permission to access data from commercial wearable devices they may have been wearing around the time of the procedure. For subcohorts of 66-118 patients who reported having a weight loss procedure and who had dense Fitbit data around their procedure date, we examined several daily measures of behavior and physiology in the 12 weeks leading up to and the 12 weeks following their procedures. We found that the weeks following weight loss operations were associated with fewer daily total steps, smaller proportions of the day spent walking, lower resting and 95th percentile heart rates, more total sleep time, and greater sleep efficiency. We demonstrate that consumer-grade activity trackers can capture behavioral and physiological changes resulting from weight loss surgery and these devices have the potential to be used to develop measures of patients' postoperative recovery that are convenient, sensitive, scalable, individualized, and continuous.

Predicting Subjective Recovery from Lower Limb Surgery Using Consumer Wearables.

INTRODUCTION: A major challenge in the monitoring of rehabilitation is the lack of long-term individual baseline data which would enable accurate and objective assessment of functional recovery. Consumer-grade wearable devices enable the tracking of individual everyday functioning prior to illness or other medical events which necessitate the monitoring of recovery trajectories. METHODS: For 1,324 individuals who underwent surgery on a lower limb, we collected their Fitbit device data of steps, heart rate, and sleep from 26 weeks before to 26 weeks after the self-reported surgery date. We identified subgroups of individuals who self-reported surgeries for bone fracture repair (n = 355), tendon or ligament repair/reconstruction (n = 773), and knee or hip joint replacement (n = 196). We used linear mixed models to estimate the average effect of time relative to surgery on daily activity measurements while adjusting for gender, age, and the participant-specific activity baseline. We used a sub-cohort of 127 individuals with dense wearable data who underwent tendon/ligament surgery and employed XGBoost to predict the self-reported recovery time. RESULTS: The 1,324 study individuals were all US residents, predominantly female (84%), white or Caucasian (85%), and young to middle-aged (mean age 36.2 years). We showed that 12 weeks pre- and 26 weeks post-surgery trajectories of daily behavioral measurements (steps sum, heart rate, sleep efficiency score) can capture activity changes relative to an individual's baseline. We demonstrated that the trajectories differ across surgery types, recapitulate the documented effect of age on functional recovery, and highlight differences in relative activity change across self-reported recovery time groups. Finally, using a sub-cohort of 127 individuals, we showed that long-term recovery can be accurately predicted, on an individual level, only 1 month after surgery (AUROC 0.734, AUPRC 0.8). Furthermore, we showed that predictions are most accurate when long-term, individual baseline data are available. DISCUSSION: Leveraging long-term, passively collected wearable data promises to enable relative assessment of individual recovery and is a first step towards data-driven intervention for individuals.

A systematic review of feasibility studies promoting the use of mobile technologies in clinical research.

Mobile technologies, such as smart phone applications, wearables, ingestibles, and implantables, are increasingly used in clinical research to capture study endpoints. On behalf of the Clinical Trials Transformation Initiative, we aimed to conduct a systematic scoping review and compile a database summarizing pilot studies addressing mobile technology sensor performance, algorithm development, software performance, and/or operational feasibility, in order to provide a resource for guiding decisions about which technology is most suitable for a particular trial. Our systematic search identified 275 publications meeting inclusion criteria. From these papers, we extracted data including the medical condition, concept of interest captured by the mobile technology, outcomes captured by the digital measurement, and details regarding the sensors, algorithms, and study sample. Sixty-seven percent of the technologies identified were wearable sensors, with the remainder including tablets, smartphones, implanted sensors, and cameras. We noted substantial variability in terms of reporting completeness and terminology used. The data have been compiled into an online database maintained by the Clinical Trials Transformation Initiative that can be filtered and searched electronically, enabling a user to find information most relevant to their work. Our long-term goal is to maintain and update the online database, in order to promote standardization of methods and reporting, encourage collaboration, and avoid redundant studies, thereby contributing to the design and implementation of efficient, high-quality trials.

Advancing the Use of Mobile Technologies in Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative.

Mobile technologies offer the potential to reduce the costs of conducting clinical trials by collecting high-quality information on health outcomes in real-world settings that are relevant to patients and clinicians. However, widespread use of mobile technologies in clinical trials has been impeded by their perceived challenges. To advance solutions to these challenges, the Clinical Trials Transformation Initiative (CTTI) has issued best practices and realistic approaches that clinical trial sponsors can now use. These include CTTI recommendations on technology selection; data collection, analysis, and interpretation; data management; protocol design and execution; and US Food and Drug Administration submission and inspection. The scientific principles underpinning the clinical trials enterprise continue to apply to studies using mobile technologies. These recommendations provide a framework for including mobile technologies in clinical trials that can lead to more efficient assessment of new therapies for patients.

Caracterización clínica epidemiológica de pacientes con EPOC y cáncer de pulmón / Clinical epidemiological characterization of patients with COPD and lung cancer

Introducción: el cáncer de pulmón es la neoplasia más frecuente y la primera causa de muerte por cáncer actualmente. Objetivo: caracterizar clínica y epidemiológicamente a pacientes fumadores con EPOC y cáncer de pulmón no microcítico. Método: estudio observacional, retrospectivo, transversal a pacientes egresados con diagnóstico de cáncer de pulmón y portadores de EPOC, durante el periodo comprendido entre enero de 2009 y enero de 2015. De 112 individuos fueron seleccionados 55 pacientes. Fueron analizadas variables sociodemográficas, intensidad del tabaquismo y del efecto acumulativo del tabaco. Además de las medidas de resumen se empleó análisis de varianzas para establecer la correlación lineal entre tabaquismo, EPOC y cáncer de pulmón. Resultados: la mayoría de los pacientes con cáncer de pulmón y EPOC pertenecían al sexo femenino (85,7 por ciento) y el 75 por ciento de los que portaban ambas afecciones se encontraba en grupos de edades menores de los 60 años. La edad de iniciación temprana del hábito, tiempo fumando, y número de paquetes de cigarrillos consumidos al año, fueron las variables que mostraron una asociación más significativa con ambas entidades (p= 0,01; 0,00; y 0,02 respectivamente). Conclusiones: la coexistencia de EPOC y cáncer de pulmón resultó más frecuente en el sexo femenino y en edades inferiores a los 60 años. Hubo mayor representación en pacientes con mayor intensidad del tabaquismo. Existió correlación entre el efecto acumulativo del tabaquismo y las probabilidades de desarrollar EPOC y cáncer en fumadores, en especial con los de mayor tiempo de consumo y los que fuman mayor número de paquetes al año(AU)

Introduction: Lung cancer is the most frequent neoplasm and the first cause of cancer death today. Objective: To characterize clinically and epidemiologically smokers with COPD and non-small cell lung cancer. Method: An observational, retrospective, cross-sectional study of patients with a diagnosis of lung cancer and COPD, during the period between January 2009 and January 2015. From 112 individuals, 55 patients were selected. Sociodemographic variables, intensity of smoking and the cumulative effect of tobacco were analyzed. In addition to the summary measures, variances analysis was used to establish the linear correlation between smoking, COPD and lung cancer. Results: Most of the patients with lung cancer and COPD belonged to the female sex (85.7 percent) and 75 percent of those with both conditions were in groups under the age of 60 years. The age of early initiation of habit, smoking time, and number of packets of cigarettes consumed per year were the variables that showed a more significant association with both entities (p = 0.01, 0.00, and 0.02 respectively). Conclusions: the coexistence of COPD and lung cancer was more frequent in females and in children under 60 years of age. There was greater occurrence in patients with greater intensity of smoking. There was a correlation between the cumulative effect of smoking and the likelihood of developing COPD and cancer in smokers; especially those with the longest consumption time and those who smoke the greatest number of packages per year(AU)

Nitrosative Stress in the Rat Retina at the Onset of Streptozotocin-Induced Diabetes.

BACKGROUND/AIMS: Nitric oxide is a multifunctional molecule that can modify proteins via nitrosylation; it can also initiate signaling cascades through the activation of soluble guanylate cyclase. Diabetic retinopathy is the leading cause of blindness, but its pathogenesis is unknown. Multiple mechanisms including oxidative-nitrosative stress have been implicated. Our main goal was to find significant changes in nitric oxide (NO) levels and determine their association with nitrosative stress in the rat retina at the onset of diabetes. METHODS: Diabetes was induced by a single intraperitoneal administration of streptozotocin. The possible nitric oxide effects on the rat retina were evaluated by the presence of nicotinamide adenine dinucleotide phosphate diaphorase (NADPH-d), a specific marker for NO-producing neurons, detected by histochemistry performed on whole retinas and retina sections. Immunohistochemistry was also performed on retina sections for iNOS, 3-nitrotyrosine (3-NT) and glial fibrillary acidic protein (GFAP). Retinal nitric oxide levels were assessed by measuring total nitrate/nitrite concentrations. Retinal cGMP levels were determined by radioimmunoassay. Western blots for nitrotyrosine (3-NT) and oxidized proteins were performed. RESULTS: In the present study, we found increased activity of NADPH-diaphorase and iNOS immunoreactivity in the rat retina at the onset of diabetes; this increase correlated with a remarkable increase in NO levels as early as 7 days after the onset of diabetes. However, cGMP levels were not modified by diabetes, suggesting that NO did not activate its signaling cascade. Even so, Western blots revealed a progressive increase in nitrated proteins at 7 days after diabetes induction. Likewise, positive nitrotyrosine immunolabeling was observed in the photoreceptor layer, ganglion cell layer, inner nuclear layer and some Müller cell processes in the retinas of diabetic rats. In addition, levels of oxidized proteins were increased in the retina early after diabetes induction; these levels were reduced by the administration of L-NAME. In addition, stress in Müller cells was determined by immunoreactivity to the glial fibrillary acidic protein. CONCLUSIONS: Our findings indicated the occurrence of nitrosative stress at the onset of diabetes in the rat retina and emphasized the role of NO in retinal function and the pathogenesis of retinopathy.