Harmful algal blooms (HABs) pose water quality risks, including the depletion of dissolved oxygen and human health impacts. Remote sensing is a proven tool for monitoring HABs, yet knowledge is limited about its effectiveness in pond-sized waterbodies, whose size and shape may preclude multi-spectral platforms with large spatial resolutions and increase the probability of mixed pixels. This comparative limnology case study evaluates whether optical remote sensing is a viable tool to monitor HABs in pond-sized waterbodies. We use Sentinel-2 imagery with previously studied chlorophyll-a and cyanobacteria detection algorithms and performed targeted in situ sampling in four small waterbodies in Boulder, CO, USA, from June to August 2021 to validate the algorithms and better understand underlying biogeochemical processes. The chlorophyll-a algorithm indicated persistent algal growth occurred in all waterbodies, yet only Sombrero Marsh chlorophyll-a expressed a statistically significant relationship with the remote sensing output (p < 0.0005, r2 = 0.80). Meanwhile, the cyanobacteria algorithm resulted in false negatives, only showing potential cyanobacteria at Sombrero Marsh despite in situ samples from all waterbodies indicating cyanobacteria were present. Samples from Sombrero Marsh had the highest chlorophyll-a (average = 132.5 µg/L) and percent cyanobacteria (average = 43.5%). These findings suggest that there is uncertainty in relying on remote sensing for monitoring HABs in small waterbodies unless a high concentration of algae is present on the water surface. However, in a resource- and time-limited system, remote sensing can be a useful tool as an initial assessment for monitoring algal blooms.
Blood biomarkers are central to monitoring disease progression and evaluating treatment responses, yet traditional venipuncture captures a single physiological snapshot in time and becomes burdensome with repeated sampling. Remote blood self-sampling offers a path toward longitudinal, decentralized monitoring, but maintaining protein integrity from draw to analysis remains a critical challenge. Here, we optimized pre-analytical blood collection and stabilization parameters to maintain protein levels at the time of collection for use with remote sampling technology. First, we optimized blood collection time with Tasso remote self-sampling devices to minimize interference from clotting, finding that a 2.5 min collection time best reduces clot formation while collecting enough blood. Next, we found that Protein Plus, a commercial protein stabilizer, limited hemolysis (a metric for stabilizer efficacy) in venous blood for up to 5 days at 25°C-35°C and for 1 day at 40°C. In addition, we optimized the stabilizer volume and acceptable blood volume range for self-sampling as the stabilizer efficacy is impacted by the stabilizer to blood ratio and collection volume can vary with remote self-sampling devices. Finally, we incubated stabilized blood samples collected via Tasso device at 25°C-35°C for 72 h, mimicking a 2-day shipping period. Using a panel of 21 inflammatory proteins, we found that Protein Plus limited intracellular protein release for various proteins (e.g., VEGF-A, CCL11, and IL-8), inhibited protein degradation for CCL2, and enabled minimal hemolysis. These results support Protein Plus as a viable stabilization strategy for remote blood collection technology targeting longitudinal inflammatory protein monitoring.
The rising prevalence of Mild Cognitive Impairment (MCI) demands effective early interventions to delay progression to dementia. This randomized controlled trial evaluated the effects of remote cognitive-motor dual-task training on cognitive function and brain functional connectivity in older adults with MCI. Linear mixed-effects models (subjects as random effects; group, time, and their interaction as fixed effects) revealed significant Group × Time interactions for MoCA scores, Mini-Mental State Examination (MMSE) scores, and brain functional connectivity (FC) (all P < 0.001), indicating that intervention effects differed across groups over time. Post-hoc comparisons showed that the Remote Cognitive-Motor Group (RCMG) achieved significant improvements in both MoCA and MMSE scores (both P < 0.001), and these gains significantly exceeded those of the Control Group (CG) (P < 0.001). The Remote Cognitive Group (RCG) showed a significant improvement in MoCA (P < 0.001), whereas no significant improvement was observed in MMSE (P = 0.188). For brain FC, the RCMG showed significantly greater post-intervention enhancement than the CG (P < 0.001). Although the RCG showed a nominally significant interaction effect (P = 0.016), this did not remain significant after correction for multiple comparisons, and no significant difference was observed between RCMG and RCG. Region-of-interest (ROI) analyses revealed that the RCMG exhibited significantly enhanced FC between multiple prefrontal and motor-related regions, including the mPFC, DLPFC, and PMC (P < 0.05).Trial registration Study on rehabilitation training of cognitive-motor dual tasks for aging-related cognitive decline (ChiCTR2200064684) and the registration date was 10/14/2022.
Prior distributions must be specified for the parameters of interest in a Bayesian clinical trial. When existing evidence on the effects of the trial interventions is limited or inconclusive, prior distributions can be constructed with expert elicitation. However, conventional elicitation requires face-to-face interactions and intensive pre-elicitation training, which can be infeasible and costly. Our remote elicitation was based on an established expert elicitation methodology, and we incorporated bivariate prior distributions to introduce dependencies between the elicited probabilities. We aimed to elicit a prior distribution for the Croup Dosing Trial, which assesses the efficacy of two separate doses of dexamethasone on the number of return visits to the emergency department within 7 days in children with croup. This trial evaluates the non-inferiority of 0.15 mg/kg of dexamethasone, compared to the standard dose of 0.60 mg/kg to treat croup. We conducted three remote workshops to elicit expert beliefs on the efficacy of the two doses of dexamethasone. Each workshop consisted of two survey rounds, separated by a group discussion. Prior to the workshop, experts reviewed the same current literature that was provided on the effects of the two doses of dexamethasone. Beliefs were aggregated using expert-specific bivariate distributions with latent effects. The aggregated distribution, along with the surveyed non-inferiority margin, determined the sample size for the Bayesian non-inferiority trial design. Twelve emergency medicine physicians participated in our remote elicitation exercise. The elicitation generated a prior distribution centered at 6% for the 0.60 mg/kg dose and 8% for the 0.15 mg/kg dose. The aggregated prior distribution produced a sample size of 1850, based on a non-inferiority margin of 4%. We elicited a prior distribution that incorporated past evidence and expert opinion. The elicited prior is consistent with previous literature on the efficacy of the dexamethasone doses in treating croup. Our approach demonstrates the feasibility of remotely eliciting bivariate distributions to design clinical trials. NCT06272383 (Registered May 8, 2024).
PurposePatient-reported outcome measures (PROMs) are valuable tools for capturing how oral chemotherapy affects patients' quality of life (QOL), yet their integration into routine clinical practice remains limited. This study aimed to evaluate the feasibility of using remote electronic PROMs (ePROMs) to collect QOL data from patients receiving oral anti-cancer (OAC) therapy. Specifically, we assessed patient response rates to text message prompts linking to a web-based EORTC QLQ-C30 survey and examined the practicality of incorporating text-based communication into clinical practice.Patients and MethodsWe conducted a prospective pilot study involving 64 patients (mean age 60 years; range 29-84) diagnosed with breast, colorectal, prostate, or lung cancer and actively receiving OAC therapy. Participants received automated monthly text message reminders over a 6-month period, directing them to complete the EORTC QLQ-C30 questionnaire online.ResultsThe initial survey response rate was 98.4%, with monthly survey completion rates ranging from 83.6% to 96.3%. Despite overall strong engagement, some noncompliance occurred due to technical issues, forgetfulness, or early withdrawal. Across the study period, 38 significant QOL changes were identified, with a median provider notification time of 1 day (range 0-89 days). Patient satisfaction surveys revealed generally positive perceptions, with participants highlighting ease of use, convenience, and appropriate survey frequency.ConclusionText message-based ePROM collection is a feasible approach for monitoring QOL in patients on OAC therapy, though completion rates vary. While patient satisfaction is high, opportunities remain to streamline the process and better integrate it into existing clinical workflows.
Climate change is accelerating spatially complex transformations in land, water, coasts, cryosphere, and ecosystems, creating a critical need for reliable, scalable, and timely monitoring based on Earth observation imagery. Conventional approaches that rely on sparse in-situ measurements, manual image interpretation, and simple spectral indices or thresholding often fail to capture subtle, heterogeneous, and multiscale changes, and they do not scale to today's multi-sensor, multi-temporal satellite archives. This review synthesizes image processing and AI techniques applied to optical, SAR, thermal, and hyperspectral remote sensing for climate change detection, covering classical change detection methods, machine learning classifiers, deep learning architectures (including Siamese and segmentation networks), spatio-temporal models for satellite image time series, and multi-sensor fusion, across application domains such as land use/land cover (LULC) and deforestation, hydrology and flooding, coastal and mangrove dynamics, cryospheric change, urban heat, ecosystems, and natural hazards. In addition, we analyze how these methods are evaluated using common performance metrics-Overall Accuracy (OA), precision, recall, F1-score, Intersection over Union (IoU), Kappa coefficient, and error measures such as RMSE-and discuss key challenges related to data quality and annotation, domain shift and generalization, computational and operational constraints, interpretability, and integration with climate and impact models. The distinctive contribution of this review is a unified method-application taxonomy that explicitly links algorithm families to specific climate monitoring tasks, a systematic comparison of reported performance metrics that clarifies trade-offs between techniques under different data and class-imbalance conditions, and a practical decision framework to guide researchers and practitioners in selecting appropriate image processing and AI approaches for given sensors, regions, and operational requirements, while outlining promising future directions such as foundation models, standardized benchmarks, and interoperable climate decision-support systems. Across the reviewed literature, deep learning approaches consistently demonstrate higher accuracy (e.g., improved IoU and F1-scores) in complex and heterogeneous environments, while classical methods remain effective for large-scale and data-scarce applications. However, significant gaps persist in model generalization across regions, availability of labeled datasets, and integration of multi-sensor time-series data.
Climate change and population growth present significant challenges to global food security, underscoring the critical importance of sustainable and efficient agricultural production. Crop rotation is a key agricultural practice that enhances food production, improves soil fertility, reduces pest and disease pressure, and maintains agro-ecological balance. The complexity and diversity of cropping patterns, particularly in the fragmented farmland of southern China, limit the availability of high-resolution crop rotation maps in precision agriculture. To improve the consistency between cropping intensity (CI) estimation and crop pattern (CP) mapping, this study developed a hierarchical framework for extracting cropland, CI, and CP from remotely sensed images. Using the Google Earth Engine (GEE) platform, a 10-m binary cropland/non-cropland map was first generated from the time-series Normalized Difference Vegetation Index (NDVI). Then, CI was derived within cropland regions using an intelligent algorithm that counts the number of growth cycles. Finally, taking advantage of crop phenology and CI constraints, nine cropping patterns were extracted from a diversified cropping region. Comparing with field survey data, the results revealed overall accuracies of 98.97%, 96.47%, and 87.92% for the cropland/non-cropland map, cropping intensity map, and cropping pattern map, respectively. These findings demonstrate the reliability of the generated maps and the potential of the proposed framework for revealing diverse cropping patterns in complex cropping regions.
The coronavirus disease 2019 (COVID-19) pandemic spurred a tremendous increase in the adoption and use of remote patient monitoring (RPM) for hypertension (HTN) management. However, limited evidence exists on the associations between frequency of utilization and uncontrolled blood pressure (BP). The present study comprehensively explores the associations between RPM use frequency and uncontrolled BP among a metropolitan-dwelling sample of hypertensive patients. Of 2,920 participants from a single urban health system, we employed a range of analytical perspectives to evaluate the RPM utilization-uncontrolled BP relationship across widely used engagement metrics: Frequency of BP transmission, digitally enabled clinician interactions, patient portal interactions, and a composite measure of utilization. Our dichotomized primary and secondary endpoints were BP >140/90 mm Hg and BP >130/80 mm Hg. Fifty-nine percent of participants were females (59%), one-third (37%) were ≥65 years old, and Hispanic patients were most represented (39%). Our primary uncontrolled BP endpoint demonstrated strong adjusted associations with suboptimal RPM use across dichotomized measures: Low BP transmission (odds ratio [OR]: 2.02, 95% confidence interval [CI]: 1.41-2.96), low clinician interactions (OR: 1.83, 95% CI: 1.43-2.36), low patient portal interactions (OR: 1.83, 95% 1.46-2.30), and low overall engagement (OR: 3.50, 95% 2.77-4.46). Our causal evaluations mirrored these findings, showing moderate causal associations after comprehensive adjustment for confounding. Assessments using other data types, such as continuous and quartiles, showed significant associations and an apparent dose-response relationship, though not at a similar magnitude. We observed strong associations between low RPM utilization and uncontrolled BP, with promising implications for patients with collectively high RPM use. These findings highlight the need to strengthen digital inclusion initiatives to improve RPM uptake and support existing efforts aimed at developing RPM clinical practice guidelines and expanding RPM reimbursement policies. Further research is warranted across diverse utilization components to better understand the linkages between engagement frequency and improved clinical outcomes.
An efficient and remote ε',ε'-regioselective (4 + 1) annulation reaction between pyrrolidinone-based Morita-Baylis-Hillman (MBH) carbonates and 1-heterodienes was pioneered in the presence of triphenylphosphine (PPh3). This approach enables the facile synthesis of a wide variety of highly substituted 1,6-diazaspiro[4.4]nonenes bearing bis-N-substituted quaternary carbon centers, achieving high yields (up to 90%) along with excellent regioselectivities. Moreover, the practical applicability of this methodology is substantiated by its successful implementation in subsequent synthetic transformations and the late-stage functionalization of drug molecules. Additionally, a molecular docking study was carried out to explore the potential biological activities of the synthesized products.
Flap transplantation plays a vital role in wound reconstruction. However, the mechanisms by which remote ischemic preconditioning (RIPC) may improve flap survival remain incompletely understood. Rats were randomly assigned to three groups: sham, ischemia/reperfusion (I/R), and RIPC + I/R. The I/R model was established by ligating the iliopsoas and thoracodorsal arteries to induce flap ischemia, followed by reperfusion. RIPC was performed via limb clamping. A combination of high-throughput sequencing, functional cellular assays, and live imaging was used to assess gene expression, cellular functions, and flap viability. RIPC upregulated the expression of ZNF667. This protein acted as a transcriptional repressor of VHL by binding to its promoter region, where it competitively inhibited the recruitment of histone-modifying enzymes, including MLL3/4, SETD1A, and EP300. Consequently, histone methylation and acetylation were reduced, leading to suppressed VHL transcription. The downregulation of VHL diminished the ubiquitination-mediated degradation of hypoxia-inducible factor-1α (HIF-1α), which in turn enhanced the expression of stromal cell-derived factor 1 (SDF1). This signaling cascade promoted the proliferation, migration, differentiation, and tube-forming capacity of endothelial progenitor cells (EPCs). Live imaging confirmed that RIPC stimulated the recruitment of EPCs into the flap tissue, accompanied by increased microvessel density. These effects collectively enhanced angiogenesis and significantly reduced the area of flap necrosis. RIPC improves flap survival by modulating the ZNF667-VHL-SDF1 axis and augmenting the function of EPCs. These findings not only provide a potential therapeutic strategy for flap transplantation but also advance our understanding of the mechanisms underlying flap survival.
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Premium tea cultivation is highly vulnerable to climate change, yet its future suitability remains insufficiently understood. In this study, we integrated spatially de-biased tea occurrence records derived from Gaofen-6 imagery and a U-Net deep learning framework with the MaxEnt model to project tea suitability in Nanping, Southeastern China, under multiple CMIP6 climate scenarios from 2021 to 2080. Random forest was used to cross-check model robustness. The results show that precipitation seasonality and precipitation of the wettest quarter are the main climatic drivers of tea suitability, and future warming is likely to shift highly suitable areas southward while increasing spatial fragmentation under high-emission scenarios. These findings provide a transferable framework for evaluating climate-sensitive high-value crops and support more adaptive agricultural planning under global environmental change.
Growing evidence shows that peer-support and peer-led counselling is beneficial for people with antenatal depression, however, little is known about the experiences of peer counsellors (PCs) in providing support. Understanding PCs' experiences is essential for determining whether a peer-led model of care is appropriate for improving access to mental health support in pregnancy. This qualitative research aimed to capture the personal impact and experiences of PCs delivering Behavioral Activation Therapy for antenatal depression within a randomized trial. Participants were recruited from the Prediction, Prevention & Interventions for Preterm Birth (P3 Cohort) involved in the Peer-Administered Support for Antenatal Depression (PAAD) Trial. Data were collected through qualitative semi-structured interviews with PCs involved in the P3 PAAD Trial. Guided by an experience-based framework, inductive thematic analysis was employed to explore the perceptions and experiences of peer counsellors with training and delivering peer counselling to pregnant Albertans. Five themes were generated, (1) Positive Fulfillment (2) Personal Growth and Learning, (3) Fostering Dialogue on Maternal Mental Health, (4) Connection and (5) Support. PCs found fulfillment in providing support, witnessing personal growth in mentees, and expressing a desire to continue their involvement. PCs experienced mutual learning with mentees, benefitted from applying learned skills to their own daily lives, and in some cases, recognized a need to further their own treatment. PCs confronted stigma and facilitated open discussions about maternal mental health while finding solidarity and support through meaningful connections with mentees and fellow counsellors. PCs felt emotionally supported, with resources and guidance readily available. Providing peer-led counselling was perceived as largely beneficial, and the results indicate that peer-led antenatal mental health support can be beneficial to those providing the support in addition to those receiving it. These findings also support further exploration of a peer-led model of care as a potential approach for improving access to antenatal mental health support.
Decentralized clinical trials (DCTs) aim to increase trial access for underrepresented populations (URP) and ensure study outcomes, conclusions, and policy-related decisions are applicable to diverse populations. To assess trends in the percentage of accrued participants from more than 120 miles from the research site upon DCT program implementation. This descriptive quality improvement study was conducted from January 2024 to March 2025 at Mayo Clinic, a US multiregional, academic medical center (AMC) comprising 3 sites and an affiliated, community-based health care system. All individuals who consented and/or accrued to an institutional review board-approved clinical trial were included. DCTs are defined as trials with at least 1 decentralized capability beyond remote consent. DCT program development began July 2022. By January 2024, remote consent, video telehealth visits, remote phlebotomy, and device services were implemented across all sites and departments. Remote monitoring and oral medication delivery were available on a limited basis. Prospective automated program and DCT participant data collection and quarterly reporting began in January 2024. Percentage of all DCT accrued participants with a residential zip code more than 120 miles from 1 of 3 AMC sites. There were 7469 participants (median [IQR] age, 62 [51-70] years; 3594 [48.1%] female; 241 [3.2%] Asian, 391 [5.2%] Black or African American, and 6347 [85.0%] White individuals) accrued to 765 DCTs. During the study period, the percentage of DCT participants residing more than 120 miles from an AMC site ranged from 18.9% in the first quarter (Q1) of 2024 (383 of 2032 participants) to 29.6% in Q1 of 2025 (346 of 1170 participants). Of those accrued, 1147 (15.4%) were of a racial or ethnic URP, ranging from 12.5% in Q1 of 2024 (253 participants) to 17.4% in Q1 of 2025 (204 participants). There were 1554 (20.8%) participants from a rural location, ranging from 16.6% in Q1 of 2024 (338 participants) to 24.9% in Q1 of 2025 (291 participants). Additionally, by geospatial distance from an AMC site, the male-to-female ratio was nearly 50:50, and the age distribution appeared similar. In this quality improvement study of a DCT program implementation, DCT accruals trended favorably among those residing more than 120 miles from an AMC site and those of rural or URP populations. While these findings cannot be directly attributed to program implementation, they are reassuring that this strategy may address some barriers to clinical trial access. Future studies should assess the sustained effectiveness and scalability of DCT models in improving equitable trial access.
The noncontact measurement of respiratory rate (RR) has gained considerable attention recently due to its relevance to remote healthcare and continuous physiological monitoring. However, existing camera-based approaches often exhibit reduced accuracy in subjects having darker skin tones, primarily owing to melanin absorption and variations in illumination that degrade remote photoplethysmographic (rPPG) signal quality. This study aims to develop a robust deep learning framework that ensures reliable RR estimation across diverse skin tones. A hybrid deep learning framework, referred to as DeepRespNet, is proposed that jointly analyzes rPPG and motion signals extracted from facial and thoracic regions in RGB video sequences. The core feature encoder, termed RespFormer, integrates spatiotemporal convolution with multi-head self-attention to capture both local and long-range respiratory patterns. The learned representations are further processed by a separate bidirectional long short-term memory (BiLSTM) network to model temporal coherence and generate physiologically stable respiratory waveforms. Optical-flow-based motion features and rPPG color variations are combined to form a multi-channel respiratory representation. The proposed framework was evaluated on a multi-subject dataset with synchronized reference respiration signals. Experimental results achieve mean absolute errors of 0.45 breaths per minute (BPM) for light-skinned subjects and 0.80 BPM for dark-skinned subjects. Bland-Altman and cross dataset analyses further confirm strong agreement and consistent performance across skin tone groups. The proposed framework enables reliable and skin-tone-aware noncontact respiratory rate estimation. Initial findings indicate its potential suitability for camera-based respiratory monitoring in remote healthcare and telemedicine applications, though further validation on larger populations is required.
Indigenous Australians in rural and remote areas experience substantial health-related quality of life (HRQoL) impacts alongside persistent healthcare access barriers. Community-led virtual primary care services offer an innovative approach to improving access to health care services for Indigenous Australians in rural and remote areas. To examine age-stratified HRQoL patterns and estimate the lifetime quality-adjusted life year (QALY) loss among Indigenous Australians with chronic conditions enrolled in a rural virtual primary care service. We conducted a cross-sectional analysis of 75 Indigenous adults residing in rural Queensland. HRQoL was measured using the EQ-5D-5L instrument. Lifetime QALY loss was calculated using Queensland Indigenous life tables and population norms, with sensitivity analyses using Australian norms and varying discount rates. Overall mean utility was 0.775 (SD = 0.246). Age-stratified analysis revealed significant heterogeneity, with three age groups (18-54, 55-64, 65-74 years) demonstrating lower HRQoL than Queensland norms. The 55-64 age group experienced poorest HRQoL (utility = 0.701, SD = 0.287) and highest projected lifetime QALY loss (4.44 QALYs undiscounted; 2.63 with 5% discount). In contrast, participants aged 75 years and above exceeded population norms (utility = 0.872 vs. 0.863). Chronic disease burden was associated with HRQoL decline in adults aged 18-64 years, while physical activity was associated with higher HRQoL in those aged 65 years and over. Indigenous Australians aged 55-64 years represent a critical priority for virtual primary care interventions. Targeted support strategies for this 'at-risk' age group are essential to address substantial lifetime health burdens and improve long-term outcomes within remote delivery models.
This study explored the use of Appreciative Inquiry to engage speech-language pathologists in co-designing culturally and contextually responsive diagnostic practices for Developmental Language Disorder in the Northern Territory, Australia. Over 18 months, Appreciative Inquiry was delivered through online sessions with Northern Territory Health speech-language pathologists. Using the 4D cycle (Discovery, Dream, Design, Destiny) and a strategic "Detour" to connect local ideas to organisational priorities, participants engaged in collaborative visioning, resource development, and guided reflection on culturally responsive, linguistically appropriate practices. The process produced a context specific framework, including guiding principles, contextual considerations, diagnostic resources, and scripts to embed child and family voice. Participants reported increased confidence applying developmental language disorder criteria, improved clarity in diagnostic decision-making, and strengthened professional connections across locations and organisations. The approach fostered ownership, flattened hierarchies, and generated practical outputs adaptable to urban, rural, and remote contexts. Appreciative Inquiry is a cost-efficient, inclusive, and flexible methodology for improving rural and remote healthcare services. By aligning locally driven solutions with strategic priorities, Appreciative Inquiry promotes sustainable practice change, collaborative leadership, and culturally and contextually responsive developmental language disorder diagnosis in the Northern Territory.
BackgroundPrenatal stress is linked to adverse perinatal outcomes. Indigenous women in Canada face high risks for adverse maternal and perinatal health.ObjectivesThis project aimed to understand levels and sources of stress experienced by Indigenous women during pregnancy and utilization of and experiences with available support.DesignThe project employed a mixed-methods cross-sectional design.MethodsThe project was conducted in three communities in Northwest Territories, Canada with varying road and healthcare accessibility. Indigenous women who were pregnant or had given birth within three years were invited to participate in a semi-structured interviewer-administered questionnaire, which included open and close-ended questions on pregnancy history, stress levels, healthcare access, available support, and experiences during and after giving birth. Descriptive statistics, regression modelling, and deductive thematic analysis were used.ResultsOf 156 participants between the ages of 17 and 47 years (mean age: 29.7 years; SD=6.0), 93.0% had given birth in the past three years, 18.0% were pregnant, and 85.3% were multiparous. Most participants reported pregnancy as somewhat stressful (49.7%) or very stressful (27.5%). Multiparity was associated with greater odds of reporting stressful pregnancy compared to first-time pregnancies (OR = 3.31, 95% CI: 1.22-8.97, p = 0.0186). Qualitative themes included reaction to pregnancy, stress during pregnancy, community support, and professional support. Factors contributing to prenatal stress included personal responsibilities, financial insecurity, housing concerns, and family issues. Support varied, with some feeling inadequately supported.ConclusionWhile various social supports exist, some participants reported inadequate support. The findings suggest the urgent need to expand community support programs in remote areas, both in numbers and access, is crucial for addressing maternal health concerns. Including kin and community supports and supporting community-driven initiatives would be effective strategies and require future exploration as to the impacts on addressing prenatal stress in Indigenous women in remote communities.
Impaired memory function is a frequent yet understudied symptom in kidney transplant recipients. In part, this knowledge gap reflects the lack of scalable, sensitive, and low-burden tools for quantifying memory function in large clinical populations. The aim of this study is to evaluate a brief, remote memory assessment and to examine memory function and its clinical correlates in kidney transplant recipients compared with healthy controls. In this cross-sectional study, we demonstrate a remote, minimally burdensome memory screener, the Seattle-Groningen Memory Assessment, which estimates a patient's speed of forgetting from paired-associate learning. Participants aged 22-86 years from a large transplant cohort completed an eight-minute online memory test. Memory performance was compared between kidney transplant recipients (n = 556) and kidney donors (n = 408). Associations with demographic, clinical, and physiological variables were examined using regression analyses. Here, we show that the memory score derived from an eight-minute session is a reliable and accurate measure of an individual's ability for long-term retention. Kidney transplant recipients show more forgetting than donors. Memory scores are sensitive to demographic factors, including age and education level, and are associated with self-reported sleep quality, fatigue, and health-related quality of life. On the physiological level, more forgetting in recipients is linked to higher monocyte, neutrophil, reticulocyte, and white blood cell counts, as well as lower ferritin and greater iron deficiency. This work highlights the potential of computational memory assessment as a minimally burdensome and reliable tool for detecting cognitive impairment in complex clinical populations. Such tools may enable scalable monitoring of cognitive health and improve the detection of subtle cognitive changes relevant for disease progression and treatment evaluation. Memory problems are commonly reported by people who receive a kidney transplant, but they are often difficult to measure with traditional cognitive or neuropsychological tests. In this study, we evaluated a brief online memory task that can be completed at home in about eight minutes. The test measures how quickly newly learned information is forgotten over time. More than 500 kidney transplant recipients and about 400 kidney donors completed the assessment. We found that transplant recipients, on average, forgot information more quickly than donors. Memory performance was also related to factors such as age, education, sleep quality, fatigue, and several blood markers linked to inflammation and iron levels. Our findings suggest that short digital memory tests may offer a practical way to monitor cognitive health in people with complex medical conditions and could support future research and clinical care.