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Smartwatch and fitness band wearable consumer electronics can passively measure pulse rate from the wrist using photoplethysmography (PPG). Identification of pulse irregularity or variability from these data has the potential to identify atrial fibrillation or atrial flutter (AF, collectively). The rapidly expanding consumer base of these devices allows for detection of undiagnosed AF at scale. The Apple Heart Study is a prospective, single arm pragmatic study that has enrolled 419,093 participants (NCT03335800). The primary objective is to measure the proportion of participants with an irregular pulse detected by the Apple Watch (Apple Inc, Cupertino, CA) with AF on subsequent ambulatory ECG patch monitoring. The secondary objectives are to: 1) characterize the concordance of pulse irregularity notification episodes from the Apple Watch with simultaneously recorded ambulatory ECGs; 2) estimate the rate of initial contact with a health care provider within 3 months after notification of pulse irregularity. The study is conducted virtually, with screening, consent and data collection performed electronically from within an accompanying smartphone app. Study visits are performed by telehealth study physicians via video chat through the app, and ambulatory ECG patches are mailed to the participants. The results of this trial will provide initial evidence for the ability of a smartwatch algorithm to identify pulse irregularity and variability which may reflect previously unknown AF. The Apple Heart Study will help provide a foundation for how wearable technology can inform the clinical approach to AF identification and screening.

To evaluate factors associated with COVID-19 preventive health behaviors among adults in Mexico City and the State of Mexico. We conducted a cross-sectional survey from June to October 2020 through a structured, internet-based questionnaire in a non-probabilistic sample of adults >18 years living in Mexico City and the State of Mexico. The independent variables included sociodemographic and clinical factors; health literacy; access to COVID-19 information; and perception of COVID-19 risk and of preventive measures' effectiveness. The dependent variable was COVID-19 preventive health behaviors, defined as the number of preventive actions adopted by participants. The data were analyzed through multivariate negative binomial regression analysis. The survey was completed by 1,030 participants. Most participants were women (70.7%), had a high school or above level of education (98.8%), and had adequate health literacy and access to COVID-19 information. Only 18% perceived having a high susceptibility to COVID-19, though 83.8% recognized the disease's severity and 87.1% the effectiveness of preventive measures. The median number of COVID-19 preventive actions was 13.5 (range 0-19). The factors associated with preventive health behavior were being female, of older age, a professional worker, a homemaker, or a retiree; engaging in regular physical exercise; having high health literacy and access to COVID-19 information sources; and perceiving COVID-19 as severe and preventive measures as effective. People with high education and internet access in Mexico City and the State of Mexico reported significant engagement in COVID-19 preventive actions during the first wave of the COVID-19 pandemic.

We extent our definition of Euler Gamma function to higher Gamma functions, and we give a unified characterization of Barnes higher Gamma functions, Mellin Gamma functions, Barnes multiple Gamma functions, Jackson q -Gamma function, and Nishizawa higher q -Gamma functions in the space of finite order meromorphic functions. The method extends to more general functional equations and unveils the multiplicative group structure of solutions that appears as a cocycle equation. We also generalize Barnes hierarchy of higher Gamma function and multiple Gamma functions. With the new definition, Barnes–Hurwitz zeta functions are no longer necessary in the definition of Barnes multiple Gamma functions. This simplifies the classical definition, without the analytic preliminaries about the meromorphic extension of Barnes–Hurwitz zeta functions, and defines a larger class of Gamma functions. For some algebraic independence conditions on the parameters, we prove uniqueness of the solutions. Hence, this implies the identification of classical Barnes multiple Gamma functions as a subclass of our multiple Gamma functions.

Using a smartphone app, the investigators recruited 419,297 participants to be monitored for irregular pulses. Patterns suggesting atrial fibrillation were detected in 2161 participants who then received ECG monitoring devices to be worn for 7 days to confirm the presence or absence of atrial fibrillation.

Analytical methods for the determination of microplastics in sediments typically involve matrix drying, sieving, grinding, and flotation as part of the sample treatment. However, the real need for these steps and analytical validation studies are scarce. This work proposes a method that avoids the drying, sieving, and flotation procedures by using a direct acid attack of HNO₃/HCl (3:1) on wet sediment samples, assisted by microwave digestion. For detection, induced fluorescence using a UV camera, with Nile Red (NR) as the fluorophore and a cell phone camera for image capture were used. The results showed that when the digestion temperature was raised to 120°C, PET recovery decreased due to plastic particle fusion. However, at 60°C, microwave digestion resulted in a 97% recovery of PET particles, eliminating chitin interference and canceling cellulose fluorescence without the need for flotation. This method proved effective for monitoring plastic microparticles in sediments from the Loa River, Chile, revealing that the river is predominantly contaminated with PET microparticles, particularly upstream in the Taira area.

Randomized controlled trials (RCTs) aim to maximize statistical power while minimizing cost and recruitment burden. In practice, randomization is often stratified or restricted using demographic variables such as age and sex, while physiological heterogeneity that may influence treatment response is rarely incorporated. Consumer smartwatches are now widely used and provide continuous, real-world measurements of cardiovascular physiology and daily activity patterns, including resting heart rate, heart rate variability, sleep timing and regularity, and physical activity, capturing stable individual-level characteristics outside clinical settings. Leveraging these data, we developed Smartwatch-Informed Matching (SIM), a pre-randomization framework that groups physiologically similar participants and applies constrained randomization to assign participants to intervention and control arms. Using a prospective cohort of 4,795 individuals, we compared SIM with conventional age- and sex-based stratification. SIM improved covariate balance and increased similarity in symptom severity (Spearman ρ = 0.176 vs. 0.012) and physiological response profiles (Pearson r = 0.245 vs. 0.112). Power analyses showed that SIM reduced the sample size required to maintain statistical power by 9-18% across a range of effect sizes. These findings demonstrate that incorporating smartwatch-derived physiological similarity into pre-randomization design can enhance the efficiency and precision of randomized clinical trials. The SIM framework is also readily applicable to retrospective matched analyses that aim to reduce confounding.

Atrial fibrillation (AF) is associated with substantial morbidity and mortality. Short-term exposures to air pollution have been associated with AF triggering; less is known regarding associations between long-term air pollution exposures and AF incidence. Our objective was to assess the association between long-term exposures to air pollution and distance to road on incidence of AF in a cohort of U.S. women. We assessed the association of high resolution spatiotemporal model predictions of long-term exposures to particulate matter ( and ), sulfur dioxide ( ), nitrogen dioxide ( ), and distance to major roads with incidence of AF diagnosis, identified through Medicare linkage, among 83,117 women in the prospective Women's Health Initiative cohort, followed from enrollment in Medicare through December 2012, incidence of AF, or death. Using time-varying Cox proportional hazards models adjusted for age, race/ethnicity, study component, body mass index, physical activity, menopausal hormone therapy, smoking, diet quality, alcohol consumption, educational attainment, and neighborhood socioeconomic status, we estimated the relative risk of incident AF in association with each pollutant. A total of 16,348 incident AF cases were observed over 660,236 person-years of follow-up. Most exposure-response associations were nonlinear. was associated with risk of AF in multivariable adjusted models [ ; 95% confidence interval (CI): 1.13, 1.24, comparing the top to bottom quartile, ]. Women living closer to roadways were at higher risk of AF (e.g., ; 95% CI: 1.01, 1.13 for living within of A3 roads, compared with , ), but we did not observe adverse associations with exposures to , , or . There were adverse associations with (top quartile ; 95% CI: 1.05, 1.16, ) and (top quartile ; 95% CI: 1.03, 1.14, ) in sensitivity models adjusting for census region. In this study of postmenopausal women, and distance to road were consistently associated with higher risk of AF. https://doi.org/10.1289/EHP7683.

Inflammatory cytokines play a role in atrial fibrillation (AF). Interleukin (IL)-1β, which is targeted in the treatment of ischemic heart disease, has not been well-studied in relation to AF. Postmenopausal women from the Women's Health Initiative were included. Cox proportional hazards regression models were used to evaluate the association between log-transformed baseline cytokine levels and future AF incidence. Models were adjusted for body mass index, age, race, education, hypertension, diabetes, hyperlipidemia, current smoking, and history of coronary heart disease, congestive heart failure, or peripheral artery disease. Of 16,729 women, 3,943 developed AF over an average of 8.5 years. Racial and ethnic groups included White (77.4%), Black/African-American (16.1%), Asian (2.7%), American Indian/Alaska Native (1.0%), and Hispanic (5.5%). Baseline IL-1β log continuous levels were not significantly associated with incident AF (HR 0.86 per 1 log [pg/mL] increase, P= .24), similar to those of other inflammatory cytokines, IL-7, IL-8, IL-10, IGF-1, and TNF-α. There were significant associations between C-reactive protein (CRP) and IL-6 with incident AF. In this large cohort of postmenopausal women, there was no significant association between IL-1β and incident AF, although downstream effectors, CRP and IL-6, were associated with incident AF.

Breast cancer remains a critical global concern, underscoring the urgent need for early detection and accurate diagnosis to improve survival rates among women. Recent developments in deep learning have shown promising potential for computer-aided detection (CAD) systems to address this challenge. In this study, a novel segmentation method based on deep learning is designed to detect tumors in breast ultrasound images. Our proposed approach combines two powerful attention mechanisms: the novel Positional Convolutional Block Attention Module (PCBAM) and Shifted Window Attention (SWA), integrated into a Residual U-Net model. The PCBAM enhances the Convolutional Block Attention Module (CBAM) by incorporating the Positional Attention Module (PAM), thereby improving the contextual information captured by CBAM and enhancing the model’s ability to capture spatial relationships within local features. Additionally, we employ SWA within the bottleneck layer of the Residual U-Net to further enhance the model’s performance. To evaluate our approach, we perform experiments using two widely used datasets of breast ultrasound images and the obtained results demonstrate its capability in accurately detecting tumors. Our approach achieves state-of-the-art performance with dice score of 74.23% and 78.58% on BUSI and UDIAT datasets, respectively in segmenting the breast tumor region, showcasing its potential to help with precise tumor detection. By leveraging the power of deep learning and integrating innovative attention mechanisms, our study contributes to the ongoing efforts to improve breast cancer detection and ultimately enhance women’s survival rates. The source code of our work can be found here: https://github.com/AyushRoy2001/DAUNet .

Background Ovarian cancer is one of the most common and malignant cancers, partly due to its late diagnosis and high recurrence. Chemotherapy resistance has been linked to poor prognosis and is believed to be linked to the cancer stem cell (CSC) pool. Therefore, elucidating the molecular mechanisms mediating therapy resistance is essential to finding new targets for therapy-resistant tumors. Methods shRNA depletion of MYPT1 in ovarian cancer cell lines, miRNA overexpression, RT-qPCR analysis, patient tumor samples, cell line- and tumorsphere-derived xenografts, in vitro and in vivo treatments, analysis of data from ovarian tumors in public transcriptomic patient databases and in-house patient cohorts. Results We show that MYPT1 ( PPP1R12A ), encoding myosin phosphatase target subunit 1, is downregulated in ovarian tumors, leading to reduced survival and increased tumorigenesis, as well as resistance to platinum-based therapy. Similarly, overexpression of miR-30b targeting MYPT1 results in enhanced CSC-like properties in ovarian tumor cells and is connected to the activation of the Hippo pathway. Inhibition of the Hippo pathway transcriptional co-activator YAP suppresses the resistance to platinum-based therapy induced by either low MYPT1 expression or miR-30b overexpression, both in vitro and in vivo. Conclusions Our work provides a functional link between the resistance to chemotherapy in ovarian tumors and the increase in the CSC pool that results from the activation of the Hippo pathway target genes upon MYPT1 downregulation. Combination therapy with cisplatin and YAP inhibitors suppresses MYPT1 -induced resistance, demonstrating the possibility of using this treatment in patients with low MYPT1 expression, who are likely to be resistant to platinum-based therapy.