Diverse Populations Research Articles

Patient Enrollment to Industry-Sponsored Versus Federally-Sponsored Cancer Clinical Trials.

The conduct of cancer clinical research in the United States is supported by both private and public sponsors. Industry aims to obtain new drug approvals. Federally-sponsored trials examine a broad set of research questions that are not typically addressed by industry; these trials, which are also more commonly conducted in diverse populations, were recently shown to have contributed to gains of 14 million life-years for patients with cancer. Despite the different mandates, the proportion of patients who might participate in industry-sponsored versus federally-sponsored cancer studies is unknown. We evaluated trial enrollment patterns from 2008 to 2022 using ClinicalTrials.gov data. The ratio of enrollments attributable to industry versus federal sponsors was estimated. A large set of estimates on the basis of different combinations of study characteristics were generated. Point estimates were determined as the mean of combinations and confidence limits by the IQR. Five-year intervals were examined to smooth annual variation. In total, N = 26,080 studies were examined. The estimated enrollment ratio from 2018 to 2022 for all industry-sponsored versus federally-sponsored trials was 8.1 (IQR, 6.2-9.9). For adult trials, the ratio increased from 4.8 (IQR, 4.4-5.3) during 2008-2012 to 9.6 (IQR, 7.4-11.8) during 2018-2022; for trials in children, the ratio increased from 0.7 (IQR, 0.6-0.7) to 2.3 (IQR, 1.8-2.7). Despite increasing cancer incidence, enrollment counts for federally-sponsored trials were flat over the study period. In the United States, there is a growing reliance on industry to conduct cancer clinical research. Underinvestment in federally-sponsored research comes at a cost for both patients and researchers, with lost opportunities for scientific, clinical, and population advances.

Correlation among anxiety and depression, fear of disease progression, and social support in coronary heart disease

BACKGROUND The mental well-being of individuals with coronary heart disease (CHD) during the intensive care unit (ICU) transition period is a multifaceted and significant concern. In this phase, the individuals might encounter psychological challenges like anxiety and depression, which can impede their recuperation and potentially have lasting effects on their health. AIM To investigate the correlation among psychological factors in CHD patients in the ICU transition period. METHODS A questionnaire survey was conducted with 119 patients admitted to the ICU after coronary artery bypass grafting between March and December 2023. Variations in Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD), Fear of Progression Questionnaire-Short Form (Fop-Q-SF), and Social Support Rating Scale (SSRS) were collected and analyzed among diverse populations. We used Pearson’s correlation analysis to examine the correlation. Multiple linear regression analysis was used to explore whether these indicators influenced depression and anxiety in the patients. RESULTS The total scores for anxiety, depression, fear of disease progression, and social support were (7.50 ± 1.41) points, (8.38 ± 1.62) points, (35.19 ± 8.14) points, and (36.34 ± 7.08) points, respectively (P < 0.05). Multivariate regression analysis showed that both the level of disease progression and social support affected the level of postoperative depression and anxiety in patients. CONCLUSION The anxiety and depression levels were positively related to each dimension of phobia disease progression and negatively related to each dimension of social support among patients with CHD.

Normal Gut Microbiomes in Diverse Populations: Clinical Implications

The human microbiome is a sensor and modulator of physiology and homeostasis. Remarkable tractability underpins the promise of therapeutic manipulation of the microbiome. However, the definition of a normal or healthy microbiome has been elusive. This is in part due to the underrepresentation of minority groups and major global regions in microbiome studies to date. We review studies of the microbiome in different populations and highlight a commonality among health-associated microbiome signatures along with major drivers of variation. We also provide an overview of microbiome-associated therapeutic interventions for some widespread, widely studied diseases. We discuss sources of bias and the challenges associated with defining population-specific microbiome reference bases. We propose a roadmap for defining normal microbiome references that can be used for population-customized microbiome therapeutics and diagnostics.

Human-Computer Interaction, and Virtual Reality Applications for Memory Enhancement

Human-Computer Interaction (HCI) and Virtual Reality (VR) technologies are redefining the boundaries of cognitive enhancement, particularly in the domain of memory improvement. By immersing users in interactive virtual environments, VR offers unique opportunities to engage multiple sensory modalities and enhance memory encoding, storage, and retrieval processes. This research explores the integration of HCI principles and VR applications to develop innovative solutions for memory enhancement. Key areas of focus include the design of adaptive VR environments tailored to individual cognitive needs, the role of gamification in reinforcing memory, and the application of biofeedback for real-time user monitoring. The study also examines challenges such as user adaptability, accessibility, and the ethical considerations of using immersive technology for cognitive interventions. Through case studies and experimental findings, this research highlights the transformative potential of VR in memory training, particularly for educational purposes and memory rehabilitation. By merging HCI methodologies with VR technology, the study offers a comprehensive framework for enhancing memory, paving the way for innovative tools that benefit diverse populations.

Oral Health Complications of SCUBA Diving

OBJECTIVE This review aims to assess the specific oral health complications associated with SCUBA diving, including barodontalgia, barosinusitis, and temporomandibular joint (TMJ) disorders, and to examine the dental implications of hyperbaric oxygen therapy (HBOT) used in decompression sickness treatment. Additionally, this review identifies gaps in the literature and suggests areas for further research to improve dental care guidelines for divers. MATERIALS AND METHODS A systematic literature search was conducted using PubMed and Scopus databases from 2004 to 2024, focusing on terms such as "barodontalgia," "dental barotrauma," and "diving dentistry." From 77 initial articles, 25 studies were selected based on relevance to pressure-induced oral complications, diving-related barotrauma, and dental management for SCUBA divers. RESULTS The review identifies barodontalgia as a prevalent issue, commonly triggered by untreated caries or defective restorations, with symptoms intensified by pressure changes. TMJ disorders, linked to prolonged clenching on diving mouthpieces, and sinus-related barotrauma also emerged as significant concerns. Preventive measures, including regular dental checkups, use of resin-based cements, and customized mouthpieces, are recommended to reduce risks. Furthermore, the review highlights a need for larger, controlled studies across diverse diver populations to validate current recommendations and develop accessible guidelines, especially for recreational divers who may lack access to specialized dental care.

Abstract A026: Assessing the impact of obesity on postoperative outcomes in RCC using machine learning

Abstract Introduction Obesity is associated with an increased risk of developing renal cell carcinoma (RCC) but paradoxically correlates with improved outcomes in metastatic cases. Moreover, the data on the relationship between obesity and postoperative outcomes following nephrectomy are inconsistent. Accurate prediction of postoperative outcomes, including complications, 30-day readmissions, and mortality, is essential for improving patient outcomes in surgical procedures. In this study, we utilized ML models to predict such outcomes in RCC patients undergoing nephroureterectomy, radical nephrectomy, partial nephrectomy, and other excision procedures on the kidney using data from the National Surgical Quality Improvement Program (NSQIP; 2016- 2021). Methods A gradient-boosted tree (GBT) ML model was developed and trained to predict the primary outcomes of interest: major complications, minor complications, 30-day readmission, and mortality. Patients with a BMI ≥30 kg/m2 were considered obese, and patients with a BMI <30 kg/m2 were used as controls. The model's performance was rigorously evaluated using various measures, such as AUROC, generalized R-square, and misclassification rate. The model's predictions were validated using separate training (70%) and validation (30%) cohorts to ensure generalizability and applicability in diverse patient populations. Results In the analysis, 36,284 cases were included. Bivariate analysis revealed that obesity was associated with an increased rate of minor complications (5.48% vs. 4.41%, p<0.0001) compared to non-obese individuals with similar major complications (3.33 vs. 3.21, p=0.49), mortality (0.54 vs. 0.64, p=0.22) and 30-day readmission rates (5.74 vs. 5.83, p=0.70). The GBT model demonstrated substantial predictive power across all four outcomes. In predicting mortality, the model achieved an AUROC of 0.80 in the validation set, with a misclassification rate of 0.68%. The prediction of major complications was also robust, with an AUROC of 0.91 in the validation set and a misclassification rate of 2.2%. Similarly, the model's performance in predicting minor complications and 30-day readmissions was strong, with AUROC values of 0.70 and 0.66, respectively, in the validation sets. Conclusions Obesity was linked to a higher rate of minor complications but did not affect other postoperative outcomes in RCC patients undergoing nephrectomy. By integrating a wide range of patient-specific variables, ML models are powerful tools for clinicians to identify high-risk patients and tailor perioperative care accordingly. The interaction between RCC and the surrounding adipose tissue microenvironment may have clinical relevance, warranting further research to elucidate the role of BMI in postoperative complications. Our findings advocate for the expanded use of ML techniques in surgical decision-making, which could enhance patient safety, optimize resource use, and improve overall surgical outcomes. Citation Format: Atulya A. Khosla, Manas Pustake, Sufal Chhabra, Yanjia Zhang, Mukesh Roy, Muni Rubens, Venkataraghavan Ramamoorthy, Anshul Saxena, Ishmael A. Jaiyesimi. Assessing the impact of obesity on postoperative outcomes in RCC using machine learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A026.

Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance. By emphasizing studies conducted in Japan, we highlight the nation's contributions to the advancement of AI in MRI. In the latter part of the review, we highlight the characteristics that make Japan a unique environment for the development and implementation of AI in MRI examinations. Japan's healthcare landscape is distinguished by several key factors that collectively create a fertile ground for AI research and development. Notably, Japan boasts one of the highest densities of MRI scanners per capita globally, ensuring widespread access to the exam. Japan's national health insurance system plays a pivotal role by providing MRI scans to all citizens irrespective of socioeconomic status, which facilitates the collection of inclusive and unbiased imaging data across a diverse population. Japan's extensive health screening programs, coupled with collaborative research initiatives like the Japan Medical Imaging Database (J-MID), enable the aggregation and sharing of large, high-quality datasets. With its technological expertise and healthcare infrastructure, Japan is well-positioned to make meaningful contributions to the MRI-AI domain. The collaborative efforts of researchers, clinicians, and technology experts, including those in Japan, will continue to advance the future of AI in clinical MRI, potentially leading to improvements in patient care and healthcare efficiency.

Chrononutrition and Mental Health: Exploring Links Between Eating Patterns Circadian Rhythms and Psychological Well-being

This study investigates the relationship between chrononutrition—timing and patterns of food intake—and mental health, focusing on how circadian rhythms influence psychological well-being. Using a qualitative approach, this literature review synthesizes recent research exploring the connections between eating patterns, biological rhythms, and mental health outcomes, such as anxiety, depression, and overall mood stability. Findings indicate that irregular eating times, late-night meals, and skipping meals disrupt circadian alignment, potentially leading to mood dysregulation and decreased psychological resilience. Conversely, eating in sync with the body’s natural circadian rhythms appears to support mental health, with regular meal timing shown to enhance mood and reduce anxiety symptoms. The review also highlights the importance of specific nutrients in promoting stable circadian cycles, suggesting that diets rich in complex carbohydrates, proteins, and certain vitamins may aid in stabilizing mood. These findings underscore the need for an integrative approach to nutrition and mental health that considers both the timing and composition of meals. This review provides valuable insights into the emerging field of chrononutrition, emphasizing the potential of circadian-aligned eating as a preventive and therapeutic strategy for mental health. Further qualitative studies are recommended to explore individualized chrononutrition interventions and their effects on mental well-being across diverse populations.

Machine learning approach for detection of MACE events within clinical trial data.

Randomized controlled trials (RCTs) are the gold standard for clinical research but may not accurately reflect the impact of medicines in real-world settings. Supplementing RCTs with insights from real-world data (RWD) can address known limitations by including more diverse patient populations, additional types of sites-of-care, and practices more representative of the care most people receive. One current challenge in using RWD is the lack of an algorithmic approach to identifying outcomes. To address this, machine learning models for identifying a frequently used outcome, Major Adverse Cardiovascular Events (MACE), were developed in Clinical Trial Data (CTD). Anonymized CTD sourced from the Medidata Enterprise Data Store were used to develop model features on the condition that they would be useful for labelling MACE events and that they could also be found in RWD. These features were used to train three random forest models to identify each component of 3-point MACE in a patient's clinical trial journey. Performance metrics for the models are presented (recall = 0.72 [0.07], precision = 0.68 [0.12] - mean, [SD]) along with the top contributing features. We show that the models can be tuned specifically to replicate the adjudication panels' results and present a cost-benefit analysis for deploying such models in clinical trial settings. We demonstrate the viability of using advanced algorithms for identifying clinical outcomes in prospective clinical trials. Deployment of such models could reduce the resources required to conduct RCTs. Extending such models to RWD would facilitate approval of pragmatic clinical trials for regulatory submissions.

To the Studies on Pollution of Under Ground Water Mau City

The study focuses on the environmental evaluation and classification of the Mau City area, located in the eastern part of Uttar Pradesh, India. The investigation covers an area of approximately 20 km2 and includes a diverse population in terms of socio- economic, cultural, and geographical characteristics. The population data reveals significant growth, with a population increase from 168,716 in 1991 to 2,205,968 in 2011. Animal populations, including cattle and poultry, have also shown an increasing trend from 2018 to 2020. Fertilizer usage data between 2017 and 2020 indicates fluctuations in the consumption of nitrogen, phosphorus, and potassium, demonstrating potential agricultural impacts on the environment. Water quality analysis is a major part of this study, employing various methods to determine salinity, dissolved oxygen, biological oxygen demand (BOD), and chemical oxygen demand (COD). Salinity was measured through titration with silver nitrate, while oxygen levels were analyzed using different methods, including dissolved oxygen (DO) analysis. Results indicate variations in water quality across different sampling sites (S1-S6), with key parameters such as pH, turbidity, temperature, and bacterial contamination being recorded monthly for the years 2019 and 2020.The research also involves the collection and analysis of water samples using various devices, including water samplers, bottom samplers, and biological samplers. Methods such as the EDTA method for water hardness, the gravimetric method for sulfate determination, and the colorimetric analysis for iron and chromium concentrations were utilized to evaluate the pollutants in the water. The study provides critical insights into water quality degradation, highlighting the presence of various pollutants, including nitrates, sulfates, and heavy metals like chromium, copper, and iron.

Differences in 6-Minute Walk Distance Across Heart Disease Recurrence Risk Levels in Cardiac Rehab Patients

Background/Objectives: Cardiac rehabilitation (CR) programs are multi-component interventions comprising structured strength and cardiovascular exercise training, psychological support, education, and therapies to promote positive lifestyle changes. This study aimed to determine if there are differences in the 6-minute walk distance (6MWD) across risk groups for recurrent heart disease. Methods: This retrospective cohort study used existing data collected from electronic medical records. The 6-minute walk distance was measured at baseline (pre-6MWD) and upon the completion of the CR program (post-6MWD). Short-term cardiac event recurrence risk was determined using a two-year recurrent coronary heart disease (2yRCHD) risk percentage, calculated according to the Framingham 2yRCHD calculator. Risk was then stratified into (1) low, (2) moderate, and (3) high-risk groups. Demographic variables (e.g., age, sex, racial/ethnic group) and clinical variables (e.g., BMI, lipid panels, fasting glucose levels, comorbidities) were collected to describe the study participants and identify potential confounders. An ANOVA and ANCOVA were performed to examine the differences in 6MWD across the 2yRCHD risk groups. Results: A total of 394 CR participants’ data were included in this analysis. Ninety-nine percent of the female participants were classified as low risk for recurrent heart disease, resulting in an extremely small sample size in the moderate-risk (n = 1) and no representation (n = 0) in the high-risk group. This lack of representation made it impossible to conduct comparative analyses across all the participants or to analyze female participants separately by risk category. Consequently, only male participant data were included in the final analysis. The study showed that pre- and post-6MWD measurements were significantly different across the three 2yRCHD risk groups (p = 0.006 for pre-6MWD; p = 0.002 for post-6MWD). The ANCOVA indicated that these differences were independent of the selected covariates. Post hoc analyses revealed significant differences in 6MWDs between the low- and high-risk groups and between the moderate- and high-risk groups, but not between the low- and moderate-risk groups, for both pre- and post-6MWD measurements. Compared to the CR participants in the high-risk group, those in the low- and moderate-risk groups achieved significantly longer distances in the 6-minute walk tests. Conclusions: The observed differences in the 6MWD across short-term cardiac recurrence risk levels suggest its potential as a simple, accessible tool for assessing cardiac recurrence risk levels in community settings. Further research is needed to generalize these findings to more diverse populations and to support aging in place for older adults living with heart disease.

Systematic Review of AI-Assisted MRI in Prostate Cancer Diagnosis: Enhancing Accuracy Through Second Opinion Tools

Background: Prostate cancer is a leading cause of cancer-related deaths in men worldwide, making accurate diagnosis critical for effective treatment. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown promise in improving the diagnostic accuracy of prostate cancer. Objectives: This systematic review aims to evaluate the effectiveness of AI-based tools in diagnosing prostate cancer using MRI, with a focus on accuracy, specificity, sensitivity, and clinical utility compared to conventional diagnostic methods. Methods: A comprehensive search was conducted across PubMed, Embase, Ovid MEDLINE, Web of Science, Cochrane Library, and Institute of Electrical and Electronics Engineers (IEEE) Xplore for studies published between 2019 and 2024. Inclusion criteria focused on full-text, English-language studies involving AI for Magnetic Resonance Imaging (MRI) -based prostate cancer diagnosis. Diagnostic performance metrics such as area under curve (AUC), sensitivity, and specificity were analyzed, with risk of bias assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results: Seven studies met the inclusion criteria, employing various AI techniques, including deep learning and machine learning. These studies reported improved diagnostic accuracy (with AUC scores of up to 97%) and moderate sensitivity, with performance varying based on training data quality and lesion characteristics like Prostate Imaging Reporting and Data System (PI-RADS) scores. Conclusions: AI has significant potential to enhance prostate cancer diagnosis, particularly when used for second opinions in MRI interpretations. While these results are promising, further validation in diverse populations and clinical settings is necessary to fully integrate AI into standard practice.

MAMMOTH-Subaru. II. Diverse Populations of Circumgalactic Lyα Nebulae at Cosmic Noon

Circumgalactic Lyα nebulae are gaseous halos around galaxies exhibiting luminous extended Lyα emission. This work investigates Lyα nebulae from deep imaging of ∼12 deg2 sky, targeted by the MAMMOTH-Subaru survey. Utilizing the wide-field capability of Hyper Suprime-Cam, we present one of the largest blind Lyα nebula selections, including QSO nebulae, Lyα blobs, and radio galaxy nebulae down to the typical 2σ Lyα surface brightness of (5-10)×10−18ergs−1cm−2arcsec−2 . The sample contains 117 nebulae with Lyα sizes of 40–400 kpc, and the most gigantic one spans about 365 kpc, and is referred to as the Ivory Nebula. Combining multiwavelength data, we investigate diverse nebula populations and associated galaxies. We find a small fraction of Lyα nebulae have QSOs (∼7%), luminous infrared galaxies (LIRGs; ∼1%), and radio galaxies (∼2%). Remarkably, among the 28 enormous Lyα nebulae (ELANe) exceeding 100 kpc, about 80% are associated with UV-faint galaxies (M UV > −22), and are categorized as Type II ELANe. We underscore that Type II ELANe constitute the majority but remain largely hidden in current galaxy and QSO surveys. Dusty starburst and obscured AGN activity are proposed to explain the nature of Type II ELANe. The spectral energy distribution of stacking all Lyα nebulae also reveals signs of massive dusty star-forming galaxies with obscured AGNs. We propose a model to explain the dusty nature where the diverse populations of Lyα nebulae capture massive galaxies at different evolutionary stages undergoing violent assembly. Lyα nebulae provide critical insights into the formation and evolution of today’s massive cluster galaxies at cosmic noon.

Neurobiological Changes Induced by Mindfulness and Meditation: A Systematic Review

Background and Objectives: Meditation and mindfulness, rooted in ancient traditions, enhance mental well-being by cultivating awareness and emotional control. It has been shown to induce neuroplasticity, increase cortical thickness, reduce amygdala reactivity, and improve brain connectivity and neurotransmitter levels, leading to improved emotional regulation, cognitive function, and stress resilience. This systematic review will synthesize research on neurobiological changes associated with mindfulness and meditation practices. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, Cochrane Library, and Embase databases without any search time range. This review has been registered on Open OSF (n) GV2JY. Results: Mindfulness-Based Stress Reduction (MBSR) enhances brain regions related to emotional processing and sensory perception, improves psychological outcomes like anxiety and depression, and exhibits unique mechanisms of pain reduction compared to placebo. Conclusions: This review highlights that mindfulness, particularly through MBSR, improves emotional regulation and brain structure, reduces anxiety, and enhances stress resilience. Future research should focus on diverse populations and naturalistic settings to better understand and optimize these benefits.

A deep dive into penetrating ocular trauma: Insights from a South Indian tertiary eye care center

Penetrating ocular trauma is a significant cause of visual impairment and blindness worldwide. In India, with its vast and diverse population, understanding the epidemiology and patterns of Penetrating ocular trauma is crucial for effective prevention and management. This study aims to investigate the prevalence, characteristics, and outcomes of penetrating ocular trauma in the South Indian population. By analyzing data from a tertiary eye care center in South India, this research seeks to contribute to the existing knowledge base and inform evidence-based interventions to reduce the burden of penetrating ocular trauma in this region.To analyze the clinical profile, management, and outcomes of penetrating ocular trauma cases at a tertiary eye care center in South India.A retrospective review of medical records from January 2024 to June 2024 was conducted. Data collected included demographic details, mode of injury, clinical presentations, management approaches, and visual outcomes.Out of 150 cases, 120 were male (80%) and 30 female (20%). The majority of cases occurred in the 20-40 age group (60%). Common causes included workplace accidents (45%), domestic injuries (30%), and road traffic accidents (15%). Initial visual acuity varied from no light perception to >6/18. Surgical intervention was required in 90% of cases. Post-operative follow-up indicated that 60% achieved a visual acuity of 6/60 or better, while 20% had no light perception.Penetrating ocular trauma predominantly affects young males in South India, with workplace injuries being a major cause. Early surgical intervention improves visual outcomes. Public awareness and safety measures could reduce the incidence of such injuries.

The genetic landscape of Lynch syndrome in the Israeli population.

Deciphering the spectrum and founder disease-causing variants (DCVs) in specific populations can shape and facilitate the diagnostic process of Lynch Syndrome (LS). The aim of this report was to comprehensively update on the genetic landscape of LS in the ethnically diverse Israeli-Jewish population. The cohort included 1080 carriers from 588 families; some from underrepresented, understudied Israeli ethnic groups recruited from 8 genetic institutes and high-risk clinics throughout the country. Variant classification was performed according to the American College of Medical Genetics criteria. A total of 157 DCVs were identified, 12 are reported here for the first time, and 9 reclassified. MSH2 DCVs were identified in 286 families (49%). Most DCVs (125/157, 80%) were noted in one or two families only. Sixteen DCVs, each detected in ≥ 5 families, and accounted for LS in 378/588 (64%) families. Constitutional mismatch repair deficiency (CMMRD) was diagnosed in 7 families. Twenty-five carriers (2.3%) had an additional DCV or risk alleles in another cancer susceptibility gene. In conclusion, MMR gene variant distribution in Israel is diverse. MSH2 is most commonly mutated due to founder DCVs. Though the 16 prevalent LS-associated DCVs were frequently detected in our cohort, none of them is frequently reported in the general population. These data should facilitate variant interpretation, spouse and cascade testing.

Human Factors, Human-Centered Design, and Usability of Sensor-Based Digital Health Technologies: Scoping Review.

Increasing adoption of sensor-based digital health technologies (sDHTs) in recent years has cast light on the many challenges in implementing these tools into clinical trials and patient care at scale across diverse patient populations; however, the methodological approaches taken toward sDHT usability evaluation have varied markedly. This review aims to explore the current landscape of studies reporting data related to sDHT human factors, human-centered design, and usability, to inform our concurrent work on developing an evaluation framework for sDHT usability. We conducted a scoping review of studies published between 2013 and 2023 and indexed in PubMed, in which data related to sDHT human factors, human-centered design, and usability were reported. Following a systematic screening process, we extracted the study design, participant sample, the sDHT or sDHTs used, the methods of data capture, and the types of usability-related data captured. Our literature search returned 442 papers, of which 85 papers were found to be eligible and 83 papers were available for data extraction and not under embargo. In total, 164 sDHTs were evaluated; 141 (86%) sDHTs were wearable tools while the remaining 23 (14%) sDHTs were ambient tools. The majority of studies (55/83, 66%) reported summative evaluations of final-design sDHTs. Almost all studies (82/83, 99%) captured data from targeted end users, but only 18 (22%) out of 83 studies captured data from additional users such as care partners or clinicians. User satisfaction and ease of use were evaluated for 83% (136/164) and 91% (150/164) of sDHTs, respectively; however, learnability, efficiency, and memorability were reported for only 11 (7%), 4 (2%), and 2 (1%) out of 164 sDHTs, respectively. A total of 14 (9%) out of 164 sDHTs were evaluated according to the extent to which users were able to understand the clinical data or other information presented to them (understandability) or the actions or tasks they should complete in response (actionability). Notable gaps in reporting included the absence of a sample size rationale (reported for 21/83, 25% of all studies and 17/55, 31% of summative studies) and incomplete sociodemographic descriptive data (complete age, sex/gender, and race/ethnicity reported for 14/83, 17% of studies). Based on our findings, we suggest four actionable recommendations for future studies that will help to advance the implementation of sDHTs: (1) consider an in-depth assessment of technology usability beyond user satisfaction and ease of use, (2) expand recruitment to include important user groups such as clinicians and care partners, (3) report the rationale for key study design considerations including the sample size, and (4) provide rich descriptive statistics regarding the study sample to allow a complete understanding of generalizability to other patient populations and contexts of use.

Heart regeneration from the whole-organism perspective to single-cell resolution.

Cardiac regenerative potential in the animal kingdom displays striking divergence across ontogeny and phylogeny. Here we discuss several fundamental questions in heart regeneration and provide both a holistic view of heart regeneration in the organism as a whole, as well as a single-cell perspective on intercellular communication among diverse cardiac cell populations. We hope to provide valuable insights that advance our understanding of organ regeneration and future therapeutic strategies.

Reimagining the meninges from a neuroimmune perspective: a boundary, but not peripheral.

Recent advances in neuroscience have transformed our understanding of the meninges, the layers surrounding the central nervous system (CNS). Two key findings have advanced our understanding: researchers identified cranial bone marrow as a reservoir for meningeal immune cells, and rediscovered a brain lymphatic system. Once viewed merely as a protective barrier, the meninges are now recognized as a dynamic interface crucial for neuroimmune interactions. This shift in perspective highlights their unique role in maintaining CNS balance, shaping brain development, and regulating responses to injury and disease. This review synthesizes the latest insights into meningeal anatomy and function, with a focus on newly identified structures such as dural-associated lymphoid tissues (DALT) and arachnoid cuff exit (ACE) points. We also examine the diverse immune cell populations within the meninges and their interactions with the CNS, underscoring the emerging view of the meninges as active participants in brain immunity. Finally, we outline critical unanswered questions about meningeal immunity, proposing directions for future research. By addressing these knowledge gaps, we aim to deepen our understanding of the meninges' role in brain health and disease, potentially paving the way for novel therapeutic approaches.

Location Matters: Variations in Cloacal Microbiota Composition of Spatially Separated Freshwater Turtles.

The gut microbiota of vertebrates is malleable and may be shaped by both intrinsic and extrinsic factors. Here, the effect that geography has on the cloacal microbiota of two species of Australian freshwater chelonians, eastern longneck turtle (Chelodina longicollis) and Macquarie River turtle (Emydura macquarii), captured from waterbodies with different levels of anthropogenic pressure was investigated. We analysed the microbiota composition, structure and diversity through 16S rRNA gene amplicon sequencing. It was hypothesised that animals from less disturbed environments would harbour a more diverse cloacal microbial population. The cloacal microbiotas from 93 turtles (C. longicollis n = 78; E. macquarii n = 15), from five locations, were analysed. For both species, the most predominant phylum was Proteobacteria. Cloacal microbiota alpha diversity varied significantly between the C. longicollis from all locations, but no differences were found for E. macquarii. In C. longicollis, turtles from wetlands within the centre of Melbourne had the lowest alpha diversity metrics, while the highest alpha diversity values were seen in turtles captured from an undisturbed rural waterbody. Beta diversity, obtained by weighted UniFrac distance, showed significant differences between locations of capture for both species of turtles in this investigation. For C. longicollis, 87 biomarkers were identified responsible for explaining differences between locations, and in E. macquarii, 42 biomarkers were found. This is the first study to explore the cloacal microbiota composition of the eastern longneck turtle and gives greater insight into microbial community structures in Macquarie River turtles. Our study demonstrated that cloacal microbiota composition of freshwater turtles was significantly influenced by locality and that disrupted environments may reduce microbial diversity in C. longicollis. Interestingly, we discovered that the effects of location contrasted significantly between species for alpha diversity with differences discovered for C. longicollis but not E. macquarii. However, for both species, beta diversity was notably influenced by habitat type. These results highlight the need to interpret chelonian microbiota data in the context of geography and human disturbance of the environment.