Diverse Populations Research Articles

Building a Digital Health Research Platform to Enable Recruitment, Enrollment, Data Collection, and Follow-Up for a Highly Diverse Longitudinal US Cohort of 1 Million People in the All of Us Research Program: Design and Implementation Study

Background Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used. Objective We sought to design and build a secure, privacy-preserving, validated, participant-centric digital health research platform (DHRP) to recruit and enroll participants, collect multimodal data, and engage participants from diverse backgrounds in the National Institutes of Health’s (NIH) All of Us Research Program (AOU). AOU is an ongoing national, multiyear study aimed to build a research cohort of 1 million participants that reflects the diversity of the United States, including minority, health-disparate, and other populations underrepresented in biomedical research (UBR). Methods We collaborated with community members, health care provider organizations (HPOs), and NIH leadership to design, build, and validate a secure, feature-rich digital platform to facilitate multisite, hybrid, and remote study participation and multimodal data collection in AOU. Participants were recruited by in-person, print, and online digital campaigns. Participants securely accessed the DHRP via web and mobile apps, either independently or with research staff support. The participant-facing tool facilitated electronic informed consent (eConsent), multisource data collection (eg, surveys, genomic results, wearables, and electronic health records [EHRs]), and ongoing participant engagement. We also built tools for research staff to conduct remote participant support, study workflow management, participant tracking, data analytics, data harmonization, and data management. Results We built a secure, participant-centric DHRP with engaging functionality used to recruit, engage, and collect data from 705,719 diverse participants throughout the United States. As of April 2024, 87% (n=613,976) of the participants enrolled via the platform were from UBR groups, including racial and ethnic minorities (n=282,429, 46%), rural dwelling individuals (n=49,118, 8%), those over the age of 65 years (n=190,333, 31%), and individuals with low socioeconomic status (n=122,795, 20%). Conclusions We built a participant-centric digital platform with tools to enable engagement with individuals from different racial, ethnic, and socioeconomic backgrounds and other UBR groups. This DHRP demonstrated successful use among diverse participants. These findings could be used as best practices for the effective use of digital platforms to build and sustain cohorts of various study designs and increase engagement with diverse populations in health research.

The integration of meditation into higher education: a systematic literature review.

Objective: Reviewed empirical studies involving meditation-related interventions for college and university students during the past decade. Methods: Based on inclusion criteria, 44 studies were selected for the review and categorized into three major areas: attention, academic performance, and mental health (stress/anxiety). Areas were systemically reviewed and synthesized. Results: Review findings suggest that, while appearing to have inconsistent impacts on academic performance, meditation practice with college students demonstrates more conclusive evidence in regard to attention including mind-wandering and the reduction of stress and anxiety. Conclusion: Meditation interventions continue to show promise as alternative treatments for mental health on campuses. However, well-designed studies are still needed to answer questions on meditation practices with students, including dosage, specific techniques, and impact on diverse populations.

Unraveling the landscape of pediatric pancreatic tumors: Insights from Japan

Pediatric pancreatic tumors, though rare, pose significant diagnostic and management challenges. The recent, 22-year nationwide survey on pediatric pancreatic tumors in Japan by Makita et al offers valuable insights into this uncommon entity, revealing striking geographical variations and questioning current treatment paradigms. This editorial commentary analyzes the study's key findings, including the predominance of solid pseudopapillary neoplasms and their younger age of onset, which contrast sharply with Western data. It explores the implications for clinical practice and research, emphasizing the need for population-specific approaches to diagnosis and treatment. The revealed limited institutional experience and surgical management patterns prompt a reevaluation of optimal care delivery for these complex cases, suggesting benefits of centralizing healthcare services. Furthermore, the commentary advocates for international collaborative studies to elucidate the genetic, environmental, and lifestyle factors influencing the development and progression of pediatric pancreatic tumors across diverse populations. It also outlines future directions, calling for advancements in precision medicine and innovative care delivery models to improve global patient outcomes. Unraveling Makita et al 's findings within the broader landscape of pediatric oncology can stimulate further research and clinical advancements in managing pancreatic and other rare tumors in children.

Obesity and metabolic syndrome in a diverse pediatric kidney transplant population: which anthropometric measure best predicts arterial stiffness?

Obesity and metabolic syndrome (MS) accelerate arterial stiffening, increasing cardiovascular (CV) risk after transplant. BMI is limited by inability to differentiate muscle, fat mass, and fat distribution patterns. The aim of this study was to identify the best anthropometric measure to detect arterial stiffness as assessed by pulse wave velocity (PWV) in a racially diverse pediatric transplant population. Kidney transplant recipients 6-20years old and ≥ 6months post-transplant were prospectively enrolled. PWV was measured oscillometrically by Mobil-O-Graph. Skeletal muscle-to-fat mass ratio (SM:FM) and percent body fat (PBF) were evaluated by dual-frequency bioelectrical impedance. BMI and waist-to-height ratio (WHR) were calculated. Associations of arterial stiffness (high PWV) with obesity and MS as defined by WHR, SM:FM, PBF, and BMI were evaluated. Participants (n = 67) were 15 (IQR 11, 18) years old and 39 (IQR 10, 68) months post-transplant. Participants with SM:FM-obesity (OR 3.2) and WHR-obesity (OR 3.0) had increased odds of high PWV (p = 0.04) while PBF-obesity (OR 2.6, p = 0.09) and BMI-obesity (OR 2.2, p = 0.17) were not significant. Participants with WHR-MS (OR 12.5, p = 0.02), SM:FM-MS (OR 5.2, p = 0.03), and PBF-MS (OR 5.0, p = 0.02) had increased odds of arterial stiffness, while BMI-MS was not significant (OR 3.7, p = 0.08). Obesity is associated with arterial stiffness in a racially diverse cohort of pediatric transplant recipients. Anthropometric measures that assess body fat distribution (WHR) and body composition (SM:FM) are more strongly associated with arterial stiffness than BMI. MS has a stronger association with arterial stiffness than obesity alone, particularly when WHR is used.

Clinical utility of plasma p-tau217 in identifying abnormal brain amyloid burden in an Asian cohort with high prevalence of concomitant cerebrovascular disease.

Using an Asian cohort with high prevalence of concomitant cerebrovascular disease (CeVD), we evaluated the performance of a plasma immunoassay for tau phosphorylated at threonine 217 (p-tau217) in detecting amyloid beta positivity (Aβ+) on positron emission tomography and cognitive decline, based on a three-range reference, which stratified patients into low-, intermediate-, and high-risk groups for Aβ+. Brain amyloid status (Aβ- [n=142] vs Aβ+ [n=73]) on amyloid PET scans was assessed along with the plasma ALZpath p-tau217 assay to derive three-range reference points for PET Aβ+ based on 90% sensitivity (lower threshold) and 90% specificity (upper threshold). Plasma p-tau217 (area under the curve [AUC]=0.923) outperformed routine clinical assessments (AUC=0.760-0.819; p ≤ 0.003) and other plasma biomarkers (AUC=0.817-0.834; p < 0.001). The high-risk group showed significantly higher rates of cognitive decline than the low-risk group. Risk stratification for PET Aβ+ based on a plasma p-tau217 assay demonstrated potential diagnostic and prognostic utility in an Asian cohort with concomitant CeVD. The utility of plasma p-tau217 to detect brain amyloid beta pathology (Aβ+) was studied in an Asian cohort with concomitant cerebrovascular disease Plasma tau phosphorylated at threonine 217 (p-tau217) showed superior utility in detecting Aβ+ compared to neuroimaging measures, clinical workup, or other blood biomarkers including p-tau181, glial fibrillary protein (GFAP), and Aβ Higher plasma p-tau217 correlated with faster cognitive decline Plasma p-tau217 shows promise as an Alzheimer's disease (AD) diagnostic and prognostic biomarker in diverse populations.

Motivation to Clinical Trial Participation: Health Information Distrust and Healthcare Access as Explanatory Variables and Gender as Moderator

Background: There is significant underrepresentation in clinical trials across diverse populations. Less is known about how health system-related factors, such as relationships and trust, mediate the motivation for clinical trial participation. We aimed to investigate whether health system-related factors explain the association between sociodemographic factors and motivation for participation. Additionally, we explored whether the mediating effects differ by gender. Methods: We used the Health Information National Trends Survey 2020 cycle-4 data. Motivation for clinical trial participation, assessed through eight items, was the outcome variable (range 1–4). Predictors included age, race, ethnicity, education, general health, and depression. The health system-related explanatory variables were health information distrust, having a regular provider, and the frequency of healthcare visits. Gender was the moderator. A structural equation model (SEM) was used for the overall and gender-stratified analyses. Results: Among the 3865 participants (mean [SE] age of 48.4 [0.53] years, 51.4% women, and 24.3% non-White), older age (β = −0.170; p &lt; 0.001) and non-White race (β = −0.078; p &lt; 0.01) were negatively associated, and higher education (β = 0.117; p &lt; 0.001) was positively associated with motivation. Higher distrust (β = −0.094; p &lt; 0.01) decreased motivation, whereas having a regular provider increased motivation (β = 0.087; p &lt; 0.01). The gender-stratified SEM revealed that women, but not men, with higher distrust showed lower motivation (β = −0.121; p &lt; 0.01), and men, but not women, with a regular healthcare provider showed higher motivation (β = 0.116; p &lt; 0.01). Conclusions: Our study found that women with higher distrust showed lower motivation, while men with a regular healthcare provider demonstrated higher motivation. These gender differences highlight the need for tailored recruitment approaches that account for their distinct relationships with the health system.

Factors Associated with Adherence to Diabetic Retinopathy Screening Among Patients Attending a Nurse-Led Community Clinic in Australia: A Qualitative Study

Background/Objectives: Despite the availability of screening services, the rate of diabetic retinopathy (DR) screening continues to be suboptimal in Australia, necessitating improvement. However, improving DR screening rates requires a more comprehensive understanding of the factors influencing adherence to the screening recommendations. This study aimed to explore the factors that influence adherence to DR screening among people with diabetes attending a community screening clinic in Australia. Methods: This qualitative study included purposively patients with diabetes recruited from a nurse-led community screening clinic in Australia. Semi-structured interviews were conducted to explore barriers and enablers impacting patient adherence to DR screening recommendations. The interview data were analyzed thematically using NVivo based on the socio-ecological model, with salience identified by the frequency of the theme. Results: A total of 22 participants completed the interview, including 10 females with a mean age of 60 ± 16.2 years. The interviews identified several factors that improved adherence to DR screening guidelines, including (a) knowledge of the connection between DR and diabetes and the importance of the screening, (b) the care provider’s recommendations, and (c) pre-booked appointments and automatic invitations. Beyond these factors, clinic staff interactions, family support, fear of vision loss, flexible clinic hours, and transportation accessibility also facilitate DR screening adherence. Conclusions: The present study identified key multi-level factors influencing adherence to DR screening. While these findings from a single clinic provide valuable insights to inform screening strategies, larger multi-center studies are needed to validate their broader applicability across diverse healthcare settings and populations.

Early life microbial succession in the gut follows common patterns in humans across the globe

Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution (±3 months) for the first 1.5 years of life. Using 3154 samples from 1827 infants across 12 countries, we trained a random forest model, achieving a root mean square error of 2.56 months. We identified key taxonomic predictors of age, including declines in Bifidobacterium spp. and increases in Faecalibacterium prausnitzii and Lachnospiraceae. Microbial succession patterns are conserved across infants from diverse human populations, suggesting universal developmental trajectories. Functional analysis confirmed trends in key microbial genes involved in feeding transitions and dietary exposures. This model provides a normative benchmark of “microbiome age” for assessing early gut maturation that may be used alongside other measures of child development.

Prospective validation study of a combined urine and plasma test for predicting high-grade prostate cancer in biopsy naïve men.

Early and accurate diagnosis of prostate cancer (PC) is crucial for effective treatment. Diagnosing clinically insignificant cancers can lead to overdiagnosis and overtreatment, highlighting the importance of accurately selecting patients for further evaluation based on improved risk prediction tools. Novel biomarkers offer promise for enhancing this diagnostic process. In this study, we aimed to externally validate a previously developed urine and plasma biomarker test in a biopsy-naïve population. Urine and blood samples were prospectively collected from 362 biopsy-naïve men with suspected PC before they underwent transrectal prostate biopsies. The expression levels of a 10-gene mRNA panel were quantified using reverse transcription/quantitative polymerase chain reaction of both urine and plasma. These gene expression levels, combined with clinical features and plasma prostate-specific antigen (PSA) levels, were used to predict the presence of International Society of Urological Pathology grade group ≥ 2 PC. Complete data were available for 314 patients. The sensitivity and specificity of the biomarker test were 87% (95% CI: 79-93%) and 42% (95% CI: 36-49%), respectively. The area under the curve was 0.76 (95% CI: 0.7-0.82) for the biomarker test probability and 0.65 (95% CI: 0.59-0.72) for PSA (p = 0.02). The test's negative predictive value was 89% (CI: 81-94%). This study did not replicate the previously reported high accuracy of the biomarker test, highlighting the need for further refinement and robust external validation to ensure reliable performance across diverse patient populations.

An Analysis of the Effectiveness and Safety of Upadacitinib in the Treatment of Inflammatory Bowel Disease: A Multicenter Real-World Study

Background and Aims: Inflammatory bowel disease (IBD) requires effective treatment options. Upadacitinib, a Janus kinase 1 (JAK1) inhibitor, has shown effectiveness in trials for Crohn’s disease (CD) and ulcerative colitis (UC). This study evaluates its real-world effectiveness and safety. Methods: We conducted a multicenter retrospective cohort study in tertiary care centers, involving patients treated with upadacitinib from January 2023 to September 2024. The study included adult patients aged 18 years or older, diagnosed with UC or CD, who received at least 8 weeks of upadacitinib therapy. Treatment outcomes were evaluated using established clinical, endoscopic, imaging, histological, and laboratory parameters. Results: A total of 236 IBD patients received upadacitinib treatment. In 80 UC patients at 8 weeks, 64.0% achieved steroid-free remission, 57.6% clinical remission, and 81.8% response. Endoscopic remission was 35.8% (p = 0.039), with 63.3% response and 35.8% mucosal healing. Histological remission reached 29.2% (p = 0.009). For 156 CD patients at 12 weeks, 76.8% achieved steroid-free remission (p &lt; 0.001), 77.8% clinical remission (p &lt; 0.001), and 81.0% response. Mean CDAI decreased from 214.9 to 117.5 (p &lt; 0.001). Endoscopic remission was 19.4%, with 48.9% response and 4.9% mucosal healing. Radiological remission was 9.1% with 85.7% response. Intestinal ultrasound showed 5.7% remission and 56.7% response. Conclusions: Upadacitinib demonstrates significant real-world effectiveness and safety in IBD, particularly in biologic-resistant cases, as evidenced by high rates of steroid-free remission and clinical response. These outcomes are likely due to its targeted JAK1 inhibition, which effectively reduces inflammation and promotes mucosal healing. Future research should focus on long-term safety, comparative effectiveness with other biologics, and its application in diverse patient populations. These findings support the integration of upadacitinib into IBD management strategies.

An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques

Asthma remains a prevalent chronic condition, impacting millions globally and presenting significant clinical and economic challenges. This study develops a predictive model for asthma outcomes, leveraging automated machine learning (AutoML) and explainable AI (XAI) to balance high predictive accuracy with interpretability. Using a comprehensive dataset of demographic, clinical, and respiratory function data, we employed AutoGluon to automate model selection, optimization, and ensembling, resulting in a model with 98.99% accuracy and a 0.9996 ROC-AUC score. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) were applied to provide both global and local interpretability, ensuring that clinicians can trust and understand model predictions. Additionally, counterfactual analysis enabled hypothetical scenario exploration, supporting personalized asthma management by allowing clinicians to assess potential interventions for individual patient risk profiles. To facilitate clinical adoption, a Streamlit v1.41.0 application was developed for real-time access to predictions and interpretability. This study addresses key gaps in asthma prediction, notably in model transparency and generalizability, while providing a practical tool for enhancing personalized care. Future research could expand the validation across diverse patient populations to reinforce the model’s robustness in broader clinical environments.

AAPM Truth-based CT (TrueCT) reconstruction grand challenge.

This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction. To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases. Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1-6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9mm for lung lesions and 3.9 to 14.9mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82±12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781±11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were -950±17 HU ranging from -918 to -979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. The reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d'] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with "0" and "1" being the worst and best measured values across all cases of the disease type for all received reconstructions. The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d' from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1mm3. The overall scores demonstrated that participant "A" had the best performance in all categories, except for the metrics of d' for lung lesions and RMSE for liver lesions. Participant "A" had an average normalized score of 0.41±0.22, 0.48±0.32, and 0.42±0.33 for the emphysema, lung lesion, and liver lesion cases, respectively. The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.

Detection, quantification, and characterization of airborne Aspergillus flavus within the corn canopy.

Aflatoxin contamination of corn can occur when developing kernels are infected by the plant pathogen Aspergillus flavus. One route of infection is from airborne conidia. We executed a series of experiments within the corn canopy during two growing seasons and in two states to document the abundance and dynamics of the airborne A. flavus population. We did not observe any significant diurnal changes in the conidial density (p = 0.171) or any effect of sampler height (p = 0.882) within the canopy. Significant changes (p < 0.001) were noted during the season, with a trend towards increased airborne populations with later stages of corn development and more than a 20-fold increase from July to August. The median aflatoxigenicity of airborne isolates from a corn canopy in Texas was about 50 times higher than the corresponding population in Mississippi. It was also noteworthy that highly aflatoxigenic, weakly sporulating S-morphotypes accounted for 14-30% of the airborne isolates in Mississippi at a site with historically rare abundance of S-morphotypes. The genetic diversity was high among the 140 analyzed airborne isolates, with 76 unique haplotypes identified and 55 haplotypes occurring only in 1 isolate. Even in the context of this highly diverse population, a haplotype matching that of a commercial biocontrol strain was found in 13 of the 70 isolates from Mississippi and 1 of the 70 isolates from Texas. The airborne A. flavus population is genetically diverse (Shannon's index = 1.4 to 1.6), similar to grain samples in other surveys, and much less aflatoxigenic in Mississippi than in Texas.

Trends in ready-to-use portable electrochemical sensing devices for healthcare diagnosis.

Compared withprevious decades, healthcare has emerged as a key global concern in light of the recurrent outbreak of pandemics. The initial stage in the provision of healthcare involves the process of diagnosis. Countries worldwide advocate for healthcare research due to its efficacy and capacity to assist diverse populations. Enhanced levels of healthcare management can be attained by the implementation of rapid diagnostic procedures and cognitive data analysis. Therefore, there is a constant need for smart therapeutics, analytical tools, and diagnostic systems to improve health and well-being. The past decade witnessed enormous growth in the sensing detection systems integrated into smartphones with printed electrodes and wearable patches for the screening of various healthcare diagnostics biomarkers and therapeutic drugs. This review focuses on the expansion of point-of-care technologies and their incorporation into a broader array of portable devices, a critical aspect in the context of decentralized societies and their healthcare systems. Discussions are broadly focused on the different sensing platforms such as solid electrodes, screen-printed electrodes, and paper-based sensing strategies for the detection of various biomarkers and therapeutic drugs. We also discuss the next-generation healthcare wearable sensing device importance and future research possibilities. Finally, theportable electrochemical sensing devices and their future perspective developments towards healthcare diagnosisare critically summarized.

&lt;b&gt;Machine Learning Enabled In-Home ECG: A Review&lt;/b&gt;

Machine learning (ML)-based in-home electrocardiogram (ECG) systems have emerged as transformative tools, advancing beyond traditional cardiology methods by offering innovative techniques for cardiac care. These systems enable sustained data collection, real-time monitoring of cardiac status, and individualized treatment plans, all while minimizing the need for frequent clinic visits. By leveraging advanced analytics and ML algorithms, in-home ECG systems analyze large-scale datasets to detect patterns and anomalies that might otherwise go unnoticed, providing early alerts and improving patient outcomes. This review examines the latest trends in ML-enhanced in-home ECG technology, emphasizing its functionality in anomaly detection, continuous monitoring, and decision-making processes. The integration of ML not only enhances diagnostic precision but also opens avenues for scalable, personalized, and remote healthcare solutions. Despite these advancements, significant challenges remain, including issues related to data privacy, algorithmic biases, and the reliability of real-world implementations. Addressing these challenges is essential for optimizing the performance and ethical use of these systems. This review also explores opportunities for future research, particularly in improving algorithm robustness and addressing biases to ensure equitable and accurate cardiac care for diverse populations. By integrating state-of-the-art ML techniques, in-home ECG systems are poised to revolutionize contemporary cardiology, reducing healthcare costs and enabling a progressive shift toward accessible, patient-centered care. This comprehensive exploration highlights the potential of ML-based in-home ECG systems to redefine cardiac monitoring and treatment, contributing to the broader transformation of modern healthcare. Received: 13 September 2024 | Revised: 11 December 2024 | Accepted: 31 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Aqsa Bibi: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review &amp; editing, Visualization, Project administration. Jawwad Sami Ur Rahman: Methodology, Validation, Investigation, Resources, Writing - review &amp; editing, Supervision, Project administration.

Why Does an "Avatar Nose" Appear After Dermal Filler Injection? Insights From a Cadaveric Study on the Nasofrontal Ligament.

Nonsurgical rhinoplasty (NSR) with dermal fillers has gained popularity because of its immediate and visible results, minimal downtime, and long-lasting effects. However, complications such as filler migration can lead to the development of the "Avatar nose," a condition where the nose appears unnaturally wide and bulbous in the nasion area, disrupting facial harmony. This phenomenon is often exacerbated by the presence of a taut nasofrontal ligament, which tethers the periosteum to the dermal layer and influences nasal contour. This study aims to introduce and define the nasofrontal ligament, a previously unrecognized anatomical structure identified during cadaveric dissection, and to measure the penetration force required to traverse this ligament in the context of NSR. By exploring these aspects, the study seeks to enhance the understanding of nasal anatomy and improve the precision and outcomes of filler-based NSR. The study involved the dissection of 2 fresh-frozen human cadaveric specimens, during which the nasofrontal ligament was identified. Penetration force was measured using a motorized force tester, with a standardized cannula depth of 5 mm. The force required to traverse the nasofrontal ligament was compared with a control point devoid of ligaments and muscles. The nasofrontal ligament required a significantly higher penetration force compared with the control point, highlighting its structural integrity and potential impact on filler migration. These findings suggest that subcision of the ligament could prevent the development of the "Avatar nose" and improve the outcomes of NSR. This study introduces the nasofrontal ligament as a key anatomical structure in NSR and demonstrates its influence on filler migration. Subcision of this ligament may enhance filler stability and aesthetic outcomes. However, the study's limitations, including a small sample size and the exclusive use of Korean cadavers, suggest the need for further research across diverse populations to validate these findings and refine NSR techniques.

Co-developing, co-creating, and testing an educational mobile health application prototype on oral cancer: A mixed methods study protocol

Introduction Globally, oral cancer is the 15th deadliest cancer among humans. Diverse health education interventions have played a very significant role in the prevention of oral cancer. Of these interventions, digital interventions, especially mhealth-driven interventions, have been largely underutilized. So far, only two known educative mobile health applications (MHAs) for oral cancer exits; unfortunately, these two applications are not comprehensive and not inclusive for diverse populations. There is a need for a comprehensive and more inclusive MHA that can be used to educate the public about oral cancer. Objectives To co-develop, co-create, and test a comprehensive and inclusive MHA that can be used to educate the public about oral cancer. Methods The proposed study will be conducted and reported using the PRODUCES (PRoblem, Objective, Design, (end-) Users, Co-creators, Evaluation, Scalability) framework—this framework provides a novel approach which can be used to develop mhealth interventions through participatory action research methodologies. This study will have three parts. The first part will co-develop an eight-sectioned lay educational content on oral cancer through three-round Delphi consultations with thirty subject matter experts in the field of oral cancer and public health. Using the co-developed educational content, the second part of this study will co-create an educative MHA prototype on oral cancer through three-round Delphi consultations with twenty digital communication experts and persons at risk of oral cancer. Lastly, the third part will, through a three-arm, parallel, open-label randomized control trial, test and compare, among seventy-five young adults, the effectiveness, usability, uptake, and acceptability of the co-created MHA prototype on oral cancer against a web-based educational resource on oral cancer. Conclusion The outcomes of this study will co-produce and test an inclusive and comprehensive MHA prototype that can be used to educate diverse populations across the world about oral cancer.

Development and Internal Validation of an AI-Enabled Cuff-less, Non-invasive Continuous Blood Pressure Monitor Across All Classes of Hypertension.

Non-invasive, continuous blood pressure monitoring technologies require additional validation beyond standard cuff-based methods. This study evaluates a non-invasive, multiparametric wearable cuffless blood pressure (BP) diagnostic monitor across all hypertension classes with diverse subjects. A prospective, multicenter study assessed Nanowear's SimpleSense-BP performance, including induced and natural BP changes, significant BP variations (Systolic BP (SBP) ≥ ± 15 mm Hg and Diastolic BP (DBP) ≥ ± 10 mm Hg), and reference input value validity over 4 weeks. 303 subjects (18-83 yrs; 50.16% Female) participated in algorithmic development and validation (Normal - 35%, Prehypertensive - 24%, Stage 1 - 24%, Stage 2 - 17%). 54 subjects were tested for induced change performance, 149 exhibited significant changes, and 91 validated reference value duration. The study clinically validated a continuous, AI-based BP diagnostic monitor using non-invasive wearable data. Further testing on diverse populations and external validation are recommended. The protocol was inspired by ISO 81060-2 and IEEE 1708:2019 standards.

Impact of Telemedicine Adoption on Patient Satisfaction during Post-Pandemic Healthcare Delivery in Ethiopia

Purpose: The aim of the study was to investigate the impact of telemedicine adoption on patient satisfaction during post-pandemic healthcare delivery. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Telemedicine has improved patient satisfaction by increasing accessibility, reducing travel and wait times, and offering flexible appointments. Patients appreciated the convenience, but challenges like digital literacy gaps, limited internet access, and data privacy concerns affected satisfaction for some. Personalized provider communication significantly boosted positive experiences. Disparities in satisfaction were observed, with older adults and low-income groups facing more barriers. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), diffusion of innovations theory &amp; expectation-confirmation theory (ECT) may be used to anchor future studies on the impact of telemedicine adoption on patient satisfaction during post-pandemic healthcare delivery. Telemedicine platforms should be developed with a user-centric approach, emphasizing simplicity, accessibility, and adaptability to meet the needs of diverse patient populations, including older adults and those with limited digital skills. Policymakers should also introduce incentives such as subsidies for technology upgrades and reimbursement parity between telemedicine and in-person visits to encourage widespread adoption.

Bacteroides Fragilis Transplantation Reverses Reproductive Senescence by Transporting Extracellular Vesicles Through the Gut-Ovary Axis.

The diverse and dynamic population of microorganisms present in the gut microbiota may affect host health. There are evidences to support the role of gut microbiota as a key player in reproductive development. Unfortunately, the relationship between reproductive disorders caused by aging and gut microbiota remains largely unknown. Here, it is shown for the first time that gut microorganism Bacteroides fragilis (BF) transplantation ameliorates ovarian aging by transporting extracellular vesicles (EVs) through the gut-ovary axis. Mechanistically, miR-1246 is enriched in EVs derived from BF-treated intestinal cells, and these miR-1246-enriched EVs are transferred to ovaries, thereby effectively improving reproductive senescence by reducing oxidative stress in the ovaries. Specifically, miR-1246 reduces the ubiquitination of p62 and stabilizes the protein level of p62 by targeting E3 ligase SKP2. Then Keap1-Nrf2 complex is dissociated and Keap1 is recruited to form the p62-Keap1 complex. With the dissociation of Keap1-Nrf2 complex, Nrf2 is released and activated, thus promoting the transcription of antioxidant enzymes and relieving reproductive senescence. Collectively, the data indicates that intestinal cell-derived EVs serve as natural information carriers in the crosstalk between the gut and the ovary, and intestinal microorganism transplantation is a promising approach for the treatment of ovarian dysfunction diseases.