Biology Open

Background: Commercial spaceflight now carries civilians on short missions, yet their stress-axis physiology is poorly described. Earlier shuttle data suggest that sympathetic responses predominate after short flights and glucocorticoid responses after long ones. The neutrophil-to-lymphocyte ratio (NLR) is a low-cost readout of both. Methods: Preregistered secondary analysis of open clinical laboratory data (NASA Open Science Data Repository OSD-569 and OSD-575) from the four Inspiration4 crew members, each sampled seven times: three draws before launch, one the day after return (R+1) and three during recovery (R+45 to R+194). Log-transformed NLR was modelled with a linear mixed model with a random intercept per crew member and Kenward-Roger inference. Lymphocyte and eosinophil counts and C-reactive protein were secondary outcomes with false discovery rate correction. Results: All 28 draws were analysed. NLR at R+1 did not differ from pre-flight (fold change 1.04; 95% CI 0.81 to 1.34; p = 0.73); two crew members increased (+25% and +46%) and two decreased (-27% and -11%). During recovery NLR was 23% lower than pre-flight (fold change 0.77; 95% CI 0.64 to 0.92; p = 0.005). No secondary outcome changed after correction; lymphocytes were 18% lower at R+1 (95% CI -35% to +4%; q = 0.31). Between-person differences explained 58% of the variance. An ordinary regression ignoring repeated measures missed the recovery effect (p = 0.08). Conclusions: A 3-day orbital flight produced no consistent leukocyte stress signature one day after return. The lower NLR during recovery suggests that pre-flight values, not recovery values, deviate from the resting state, which has implications for how baselines are defined in short-duration spaceflight studies.

Purpose Autistic adults have diverse communication characteristics that are not well captured by standard demographic measures. Communication affects self-determination, relationships, access to healthcare, and services. Characterizing communication differences may improve research and intervention targeting for meaningful subgroups of autistic adults. Methods The Academic Autism Spectrum Partnership in Research and Education (AASPIRE) Outcomes Project team used a community based participatory research (CBPR) approach to develop a self- or caregiver-report module to characterize communication among autistic adults. The 10-item module asks about receptive and expressive communication, literacy, speech reliability, usage of digital communication, use of alternatives to speech, and communication fluency. It can be completed directly, with or without support, or via a caregiver. We used baseline survey data from 844 autistic adults to evaluate the module and derived a five-level variable, Communication Profile, from selected module items to stratify participants. We examined completion feasibility, response consistency, and construct validity. We also assessed test-retest reliability in a 15% random subsample who completed a 2-week retest following the 6-month survey, with oversampling of caregiver-report respondents. Results The Communication Characteristics module demonstrated high completion feasibility, minimal systematic error, and strong construct validity. The Communication Profile variable stratified participants into five meaningful communication profiles and demonstrated strong construct validity and substantial test-retest reliability ({kappa} = .69). Conclusion The Communication Characteristics module and Communication Profile variable provide a valid approach for characterizing communication diversity among autistic adults for research, clinical, and service settings. The full AASPIRE Measurement Toolkit is available at https://aaspire.org/measurement. Lay Summary Autistic people communicate in many different ways, and their communication may change in different situations. We developed and tested a set of survey questions to describe an individuals communication. We also used the results to describe 5 groups of autistic people based on how they communicate, without treating these groups as different types of autism.

Autistic adults face considerable challenges in receiving adequate support, from both paid/formal (such as from disability services) and unpaid/informal (for example, from friends and family) sources. Having adequate support can lead to better health and well-being outcomes. However, most instruments designed to assess adequacy of support were not made specifically for/with autistic adults. This paper presents a psychometric validation of the AASPIRE Adequacy of Supports and Information Scale (AASIS), a community-informed adaptation of a previously developed instrument (the Quality of Care and Support scale; Lucas-Carrasco et al., 2011), designed to be relevant and accessible to autistic adults. We also used the AASIS to explore correlates of perceptions of support in a sample of 798 autistic adults. Our confirmatory factor analysis supported a two factor structure for the AASIS (factor one: Supports; factor two: Information). Further testing confirmed the instrument was invariant across several groups, including race/ethnicity, gender, intellectual disability, communication fluency, and self versus proxy-report. Regression analyses suggested that the way in which participants joined the study (i.e., through community advertisements or via established healthcare/disability services) impacted whether they viewed their support as adequate. Additionally, a higher number of adverse childhood events and greater experience of discrimination were related to worse perceptions of support adequacy across both factors. Our findings support the preliminary validity of the AASIS across a variety of autistic people, though further testing is warranted. Findings also point to the importance of addressing the needs of autistic people who are minoritized and/or who have complex trauma histories.

Genetic regulation of neurodevelopmental disorders that impair brain development and social interaction is complex and poorly understood. Although emerging evidence suggests a link between neurodevelopmental disorders and autophagy deficits, congenital autophagy gene disorders are rare in humans, and underlying molecular mechanisms remain unknown. Here we demonstrate that the autophagy protein BECN1 is a regulator of neuronal and social development in vertebrate species. We identify rare deleterious biallelic BECN1 variants that cause neurodevelopmental disorders in two unrelated families. Becn1 disruption also causes neuronal and social developmental defects in mouse and zebrafish models. BECN1 serves as an adaptor to recruit Arc to the E3 ubiquitin ligase UBE3A for polyubiquitination and degradation, thereby maintaining postsynaptic AMPA receptor (AMPAR) stability, AMPAR-mediated excitatory neurotransmission, and normal social interaction. Thus, our study illustrates new biology and a non-canonical mechanism by which BECN1 regulates neurodevelopment in humans, mice and zebrafish, via a crosstalk with the ubiquitin-proteasomal machinery.

Introduction: Thyroid diseases are major health problems and among the most common endocrine disorders worldwide, including in Ethiopia. They are characterized by alterations in hormone secretion, enlargement of the thyroid gland, or both. Fine needle aspiration cytology is an important initial screening tool, and histopathologic examination is essential for the definitive diagnosis of thyroid disease. This study aimed to compare the diagnostic accuracy of fine needle aspiration cytology with histopathology in identifying thyroid diseases. Methods: A cross-sectional study was conducted at three health facilities in Hawassa City, Ethiopia, involving 304 patients who underwent both thyroid fine needle aspiration cytology and histopathologic examination of the same site between 30th October 2024 and 29th October 2025. FNAC results were classified using the Bethesda system, with histopathology as the gold standard. Data were entered into Kobo Toolbox, exported to SPSS version 27, and analyzed using cross-tabulation and chi-square ({chi}{superscript 2}) tests to determine diagnostic accuracy. Result: 304 patient records were included in the study. The fine needle aspiration cytology was determinate/diagnostic in 81.6% of cases. The study revealed the sensitivity and specificity of fine needle aspiration cytology to be 88.2% (CI 77.6-95.1%) and 94.4% (CI 90.6-97.1%), respectively. The false negative and false positive rates in this study were 2.4% and 4.4%, respectively. The positive predictive value was 80.3% (CI 68.7-89.3%), and the negative predictive value was 96.8% (CI 93.8-98.7%). The accuracy of fine needle aspiration cytology in differentiating benign from malignant thyroid lesions was 93.1%. Conclusion: FNAC demonstrated strong agreement with histopathology and remains a reliable, cost-effective first-line diagnostic tool for thyroid disease, particularly in resource-limited settings. Strengthening sample handling, ultrasound guidance, and adherence to Bethesda criteria may further improve diagnostic precision

PURPOSE: Women with metastatic breast cancer (MBC) are at risk of developing financial toxicity due to prolonged treatment. Resilience has been identified as a key protective factor associated with better quality of life among cancer populations. However, it is unknown if resilience is associated with financial toxicity. We sought to examine the relationship between resilience and financial toxicity among women with MBC and explore whether socioeconomic factors moderate this association. PATIENTS AND METHODS: Our sample included 209 women with MBC who completed self-reported questionnaires of the Resilience Scale Specific to Cancer (RS-SC) and the Comprehensive Score for Financial Toxicity (COST). Participants (n=2) with missing data were excluded from analysis. We conducted multiple linear regression to examine the association of resilience with financial toxicity. Interaction terms were used to test moderating effects of age (18-39, 40-64 and > =65 years), race, education, health insurance, household income and employment status. RESULTS: Of the sample, 56.5% were aged 40-64 years, 76.6% were White, and 79.9% had a college or master degree or above. Women reported moderately high resilience with mean RS-SC score of 34.49 (SD = 6.67), and moderate financial toxicity (mean COST score =21.01 [SD = 10.51]). Adjusting for covariates, higher resilience was linked to better financial well-being (b = 0.37, SE= 0.11, 95% CI [0.14, 0.59], P < 0.01). Age, education and health insurance moderated this relationship, with resilience more strongly associated with better financial well-being among those aged 40-64 years, with college/master degree or above and private insurance. Race, household income, and employment status did not moderate the association between resilience and financial toxicity. CONCLUSION: Women with MBC who reported higher resilience are more likely to experience better financial well-being, but the strength of this association varied by socioeconomic factors. Further research is needed to confirm these findings. Our results have important implications for incorporating resilience into financial toxicity interventions and tailoring such interventions based on individual socioeconomic characteristics.

Objectives: Health professions students often receive limited training on how to adjust their communication for patients whose first language is not English (LX), despite the risks for diagnosis, treatment, and patient trust. Based on Communication Accommodation Theory, we developed a brief module to improve students' awareness and knowledge of strategic communication adjustments for LX patients. Methods: We tested feasibility in a single-group, pre-post pilot study guided by the Kirkpatrick framework with undergraduate (n=22) and graduate (n=41) speech-language pathology and psychology students in Massachusetts and North Carolina. The module included instructional content, video and audio examples, interactive activities, and a podcast. Learning was assessed with knowledge checks, and self-reported facilitative and resistant accommodation with the Communication Accommodation Behavior Scale (CABS). After training, students rated their confidence and likelihood of using accommodation strategies. Results: At both sites, facilitative accommodation increased and resistant accommodation decreased significantly, with a larger decrease at the undergraduate site. Knowledge-check accuracy was high at both time points, suggesting a ceiling effect. Students reported moderate- to-high confidence in adapting their speech and high likelihood of using a learned strategy in future clinical encounters. Conclusions: A brief module can be feasibly delivered across institutions and is associated with short-term improvements in self-reported communication accommodation. Practice implications: We recommend future work evaluate whether these changes translate into clinical behavior long-term and refine knowledge-check items to better capture learning gains.

Background. Regulatory pressure to avoid confusingly similar drug names appears to have pushed manufacturers to coin more distinctive proprietary names, but no one has quantitatively analyzed this trend. Objective. To determine whether proprietary drug names became more orthographically distinctive (unusual in spelling) over time. Methods. We examined 1,321 US Food and Drug Administration (FDA) new molecular entity (NME) names (1985-2024), 2,769 FDA Orange Book trade names (1982-2026), 177,337 US pharmaceutical trademarks (1980-2026), and 1,273 generic names (1982-2025). Compared to prior names (including those already marketed) and ordinary English, we scored each name's character trigram (three-letter sequence) surprisal (statistical unexpectedness) and its edit-distance similarity to the closest existing names. Linear regression tested time trends, controlling for name length. Results. Mean NME trigram surprisal rose in every decade between the 1980s and 2020s, from 7.21 to 8.50 nats (natural-logarithm units, higher meaning less predictable), an 18% increase (b = 0.038, p < .001, R2 = 0.34). Half of 2020s names (7% in the 1980s) contained a letter sequence found in no common English word (e.g., Jesduvroq, Omvoh). Nearest-neighbor similarity fell after the early 1990s. Trademark data showed the same trend (b = 0.016, p < .001). Generic names showed a weaker trend (b = 0.011, R2 = 0.063). Conclusions. Over four decades, drug names became more orthographically distinctive, consistent with namespace crowding, deliberate efforts to avoid confusing names, and branding strategy. Because unfamiliar letter sequences can introduce other error pathways, increased distinctiveness does not necessarily imply improved safety.

Computed tomography (CT) interpretation requires recognition of the anatomical level of each slice. Classification may facilitate preprocessing and AI-assisted fracture diagnosis. We developed deep learning models to classify craniofacial CT slices into six anatomical categories. A total of 11,769 images were collected from a multicenter database involving 10 Japanese university hospitals and the public Head-Neck-Radiomics-HN1 dataset. Patients were divided into training (70%) and evaluation (30%) sets. Five CNN architectures were trained from scratch, and hard- and soft-voting ensembles were evaluated. Individual-model accuracy ranged from 94.72% to 96.09%; the hard-voting ensemble achieved 96.46% accuracy (Cohen's k = 0.954). Although performance differed among individual models (Cochran's Q = 99.29, p < 0.001), the ensemble did not significantly outperform EfficientNetV2B1 (McNemar's p = 0.163). Errors occurred mainly between adjacent anatomical regions. Deep learning can classify craniofacial CT slices accurately and may support automated dataset organization and AI-assisted fracture diagnosis. External validation is needed.

OBJECTIVE: Research on neighborhood context and cognitive health often relies on composite indices that collapse neighborhood socioeconomic status (SES) and aspects of the built environment into a single score. This study examined the extent to which neighborhood SES and built environment were associated with late-life cognitive impairment. METHOD: Participants were 281 predominantly Black older adults who completed the Self-Administered Gerocognitive Exam (SAGE), a brief cognitive screening measure. Participants also provided their home address to obtain four neighborhood metrics: Two composite measures of neighborhood disadvantage, the Environmental Justice Index (EJI) and the EJI Social-Environmental Rank, as well as the EJI Social Vulnerability Rank as a measure of neighborhood SES, and the EJI Environmental Burden Rank to measure exposure to hazardous aspects of the built environment. RESULTS: Lower neighborhood SES was associated with higher odds of cognitive impairment. In contrast, greater environmental burden was associated with lower odds of impairment, with years of education as a moderator; the association was strongest among individuals with high levels of education. The two composite scores were not associated with cognitive impairment. CONCLUSION: Composite measures of neighborhood features may obscure distinct pathways of their effects on cognition.

Objectives To evaluate discrepancies between radiologists and referring physicians in the interpretation of diagnostic probabilities conveyed by common Japanese expressions used in radiology reports. Materials and Methods Fifteen expressions commonly used to convey diagnostic probabilities in radiology reports were selected by five attending radiologists. An anonymous online survey was distributed to 26 radiologists and 33 referring physicians at a single institution. Participants assigned a diagnostic probability (from 0% to 100% in 10% increments) to each expression. Differences in median probabilities between radiologists and referring physicians were analyzed using the Mann-Whitney U test. Variability in interpretation was assessed using interquartile range (IQR), with IQR [&ge;] 30% defined as substantial variability. Results All radiologists (26/26) and 97% of referring physicians (32/33) completed the survey. Seven of the 15 expressions showed statistically significant differences in interpretation between radiologists and referring physicians: "c/w" (80% vs. 60%; p = 0.031), the Japanese equivalents of "present" (100% vs. 100%; p = 0.024), "suspect of" (80% vs. 70%; p = 0.007), "possibly" (50% vs. 70%; p < 0.001), "unlikely" (10% vs. 10%; p = 0.022), "cannot be ruled out" (25% vs. 40%; p < 0.001), and "not inconsistent with" (80% vs. 90%; p = 0.008). Substantial variability in interpretation (IQR [&ge;] 30%) was observed for four expressions: "cannot be ruled out" (IQR, 10%-40%), "s/o" (IQR, 60%-90%), "c/w" (IQR, 50%-90%), and "not inconsistent with" (IQR, 70%-100%). Conclusion This study revealed that nearly half of the Japanese expressions commonly used to convey diagnostic probabilities were interpreted differently by radiologists and referring physicians. These discrepancies highlight the need for a standardized lexicon to improve clarity and consistency in radiology reporting.

Background: School-based implementation of robot-assisted walking may support increased physical and social participation for children with physical disabilities, but evidence from an adequately powered trial is needed. This pilot study evaluated implementation strategies and study procedures to inform a future definitive trial. Methods: Participants were randomized to 12-weeks of robot-assisted walking or control, with subsequent crossover to the other condition. In the intervention arm, targeted dose was 75 minutes of robot-assisted walking, spread across three days/week. Implementation strategies for the child's classroom team included educational materials, centralized technical assistance, school champions, and on-going training. Implementation strategy evaluation focused on intervention protocol feasibility (i.e., weekly device use, troubleshooting) and fidelity (documentation of implementation strategy modifications). Preliminary estimates participation goal changes were obtained using the Canadian Occupational Performance Measure (COPM) 'Performance' scale (/10). Results: Eight children with neurodevelopmental physical disabilities participated (mean age 3.5, SD 0.5 years). Device use varied from 2.1 to 4.3 days/week (median 3.3) with a range of 48 to 96 minutes/week (median 80). Target use of >= 3 days/week was achieved for 66% of weeks and met the >=75-minute target for 33% of weeks. Troubleshooting occurred in 18.5% of sessions. Two implementation strategies were modified to improve feasibility. COPM mean change was 3.56 (SD 1.07) for robot-assisted walking and 2.11 (SD 2.41) for control (dz=0.74). Conclusions: This pilot study provided preliminary evidence regarding both the feasibility of robot-assisted walking implementation in schools and its potential effectiveness for children with physical disabilities. Findings support progression to a definitive trial.

Chronic intermittent hypobaric hypoxia is an occupational exposure experienced by workers who alternate between high-altitude worksites and lower-altitude resting locations. Its effects on cardiac autonomic modulation and electrocardiographic parameters remain insufficiently characterized in Andean workers. This study aimed to compare heart rate variability and electrocardiographic parameters between workers exposed to chronic intermittent hypobaric hypoxia and controls working permanently at lower altitude in Arequipa, Peru. An observational cross-sectional study was conducted in 96 male workers aged 20-60 years. The exposed group included 53 workers performing rotational shifts at altitudes [&ge;]3,000 m above sea level with rest periods at <2,500 m, while the control group included 43 workers working permanently at <2,500 m. Heart rate variability was assessed during the morning rest period in Arequipa, at approximately 2,300 25 m above sea level, using a Polar(R) H10 heart rate sensor after 15 minutes of rest. Participants also underwent standard 12-lead electrocardiography. Vital signs were clinically similar between groups. No statistically significant differences were observed in PR interval, corrected QT interval, time-domain heart rate variability parameters, or frequency-domain heart rate variability parameters. In this sample of apparently healthy male workers evaluated during their rest period at moderate altitude, exposure to chronic intermittent hypobaric hypoxia was not associated with significant differences in resting heart rate variability or standard electrocardiographic parameters.

Background: Self-supervised learning may improve segmentation when labelled data are limited, but its benefit may depend on model and initialisation strategy. This study evaluated in-domain masked-reconstruction pretraining across limited annotation budgets. Methods: The International Skin Imaging Collaboration (ISIC) 2016 development set was partitioned into 765 images for self-supervised pretraining, 94 for fine-tuning and 41 for validation. Five nested annotation budgets (12-94 images) and five seeds were evaluated on the 379-image test set. A custom U-Net compared random and in-domain self-supervised initialisation; a ResNet-34 U-Net compared random, ImageNet, in-domain self-supervised and sequential ImageNet-to-self-supervised initialisation. Models trained with 94 labelled images were evaluated without adaptation on ISIC 2017. A separate experiment examined restoration-based pretraining. Results: In the custom U-Net, self-supervised pretraining achieved higher mean Dice than random initialisation at all five budgets, including 0.664 versus 0.615 with 12 labels and 0.811 versus 0.768 with 94 labels. Seed-level 95% confidence intervals for paired differences excluded zero at 47 and 94 labels but not at the other budgets. With 47 labels, the self-supervised model achieved mean Dice of 0.789, compared with 0.768 for random initialisation with 94 labels. In ResNet-34, ImageNet initialisation achieved higher mean Dice than in-domain self-supervised pretraining at all five budgets, and adding self-supervised pretraining after ImageNet reduced mean Dice at four of five budgets. All six 94-label configurations achieved lower mean Dice on ISIC 2017 than ISIC 2016. In the restoration experiment, mean Dice was 0.686 with random initialisation and 0.638-0.642 following pretraining. Conclusions: In-domain masked-reconstruction pretraining improved mean Dice relative to random initialisation in the custom U-Net, but not consistently in the ResNet-34 U-Net, where ImageNet initialisation performed better at every budget. Adding self-supervised pretraining after ImageNet provided no consistent gain. These findings are specific to the dermoscopic lesion-segmentation experiments and do not establish melanoma-specific diagnostic or clinical performance.

Purpose: To compare the performance of three large language models (LLMs), ChatGPT-o1, Grok 3, and Gemini 2.0 Pro, on the American College of Prosthodontists' National Prosthodontic Resident Examination (NPRE). Materials and Methods: Official NPRE examinations from years 2015-2024 were retrieved from the American College of Prosthodontists website and used to prompt each LLM. Paid access tiers and advanced reasoning were enabled. Responses from LLMs were recorded and compared against the official answer keys for accuracy, variability across years, and consistency of responses. Duplicate questions were retained for the primary analysis and removed in a sensitivity analysis. Accuracy (percent correct) was compared using generalized estimating equations binary logistic regression (primary); Cochran's Q with Bonferroni-adjusted McNemar tests (sensitivity). Year-to-year variability (SD of annual accuracy) was compared using weighted Levene tests. Consistency across duplicate items was quantified as the percentage of perfect matches and compared using Cochran's Q with Bonferroni-adjusted McNemar tests. Results: Across 1,487 questions, overall accuracy differed among models (global P < 0.001). ChatGPT-o1 achieved 78.4% correct, Grok 3 76.4%, and Gemini 2.0 Pro 73.2%; ChatGPT-o1 and Grok 3's accuracy significantly exceeded Gemini 2.0 Pro (both P [&le;] 0.016), with no significant difference between ChatGPT-o1 and Grok 3 (P = 0.099). After removing duplicates (n = 1,200), results were similar: ChatGPT-o1, Grok 3, and Gemini 2.0 Pro were 77.1%, 76.6%, and 71.5% correct, respectively; ChatGPT-o1 and Grok 3's accuracy was significantly higher than Gemini 2.0 Pro (both P < 0.001). Variability across years did not differ significantly (P [&ge;] 0.097). Grok 3 showed significantly higher perfect-match consistency (79.2%) than ChatGPT-o1 (69.4%) and Gemini 2.0 Pro (64.5%) (P [&le;] 0.010). Conclusions: Contemporary LLMs showed 70-80% accuracy on postgraduate prosthodontic multiple-choice content. While promising for educational support, residual error and inconsistency warrant human oversight and transparent methods reporting before curricular adoption.

Exposure to armed conflict can leave persistent effects on mental health, yet its neurophysiological correlates remain poorly characterized, particularly in populations exposed to prolonged violence outside narrowly defined post traumatic stress disorder samples. Here we examined resting state electroencephalographic activity in 122 Colombian adults, including 74 participants exposed to armed conflict and 48 controls. Eyes closed EEG was analysed for 240 seconds per participant. We quantified absolute and relative spectral power across delta, theta, alpha and beta bands and regional functional connectivity using weighted phase lag index in theta, alpha and beta. Group comparisons were adjusted for age, education, sex and assessment year, with correction across 78 EEG contrasts. Median occipital and global alpha power was lower in exposed participants, although adjusted estimates were close to the null and confidence intervals were broad. Temporal theta relative power was lower in exposed participants at nominal significance, but this association did not survive Holm correction. No spectral power or regional connectivity contrast remained significant after multiple testing correction. Exploratory associations between EEG measures and MoCA performance were modest in participants with confirmed scores. These findings provide a systematic characterization of resting EEG after exposure to armed conflict in a population shaped by prolonged violence and displacement. They also indicate that large or widespread alterations in resting spectral power and connectivity were not detectable with the current sample and analytical framework, while defining candidate signals and methodological priorities for larger studies of conflict related brain health.

Objective To examine the association between patient-reported shared decision-making (SDM) and documented advance care planning (ACP) among adults with Alzheimer disease and related dementias (ADRD), and to assess whether this association differed between adults with and without ADRD. Design Retrospective observational study using linked survey and electronic health record (EHR) data. Setting and Participants Participants aged [&ge;]50 years in the All of Us Research Program who completed a patient-reported SDM measure and had linked EHR records. The primary analytic cohort included 3,635 adults with ADRD; 123,681 adults without ADRD comprised a secondary comparison cohort. Methods SDM was measured by how often clinicians asked participants about their opinions or preferences regarding medical care or treatment and was categorized as high (always/most of the time) or low (some of the time/ None of the time). Documented ACP was identified within 3 years before or after the SDM assessment. Modified Poisson regression with robust variance estimated prevalence ratios (PRs) and adjusted PRs (aPRs). Results Among adults with ADRD, 60.6% reported high SDM. Documented ACP prevalence was 5.1% among participants with high SDM and 3.6% among those with low SDM (P=0.027). High SDM was associated with greater ACP prevalence in the unadjusted model (PR, 1.44; 95% CI, 1.04-1.99); the association remained positive but was attenuated after full adjustment (aPR, 1.28; 95% CI, 0.93-1.77). Among adults without ADRD, high SDM was associated with documented ACP (aPR, 1.17; 95% CI, 1.03-1.33). Conclusions and Implications Among adults with ADRD, high SDM was associated with greater ACP prevalence in unadjusted analyses, although the fully adjusted association was not statistically significant. In contrast, higher SDM remained significantly associated with ACP among those without ADRD. The low prevalence of ACP highlights an important opportunity to expand discussions of current and future care preferences as a routine component of patient-centered care.

Fetal development is highly sensitive to environmental influences, including maternal smoking during pregnancy (MSDP) and air pollution. The placenta may play a central role in mediating these effects through epigenetic mechanisms, including small non-coding RNAs (sncRNAs). While most studies have focused on microRNAs (miRNAs), other sncRNA classes remain poorly characterized. Here, we characterized the placental sncRNA landscape and investigated its associations with prenatal exposure to MSDP and air pollution. The sncRNA expression profile of 390 chorionic villi samples from the Barcelona Life Study Cohort (BiSC) was measured using small RNA sequencing and the seqBuster/seqCluster pipeline. MSDP was assessed via questionnaire, and prenatal exposure to black carbon (BC), particulate matter [&le;]2.5 m (PM2.5), and nitrogen dioxide (NO2) was estimated using hybrid dispersion-land-use regression (LUR) spatiotemporal models. Associations were evaluated using linear regressions adjusted for relevant covariates. We identified 979 placental sncRNA clusters, assigned primarily to miRNAs (33.2%), piRNAs (26.4%), snoRNAs (12.2%), tRFs (7.8%), and others (20.4%). MSDP was associated with the downregulation of miR-421 and the upregulation of multiple snoRNAs from the imprinted DLK1-DIO3 locus (SNORD113-4, SNORD114-2, SNORD114-3, and SNORD114-17), as well as the piR-26394. No sncRNAs remained significantly associated with air pollution after multiple-testing correction, although several miRNAs showed suggestive associations. Overall, this study provides a comprehensive characterization of placental sncRNAs and identifies previously unreported smoking-associated sncRNAs within the imprinted DLK1-DIO3 locus, expanding our understanding of placental epigenetic responses to prenatal environmental exposures.

Estimating the correlation between subject-specific rates of change is a common objective in longitudinal studies. A natural approach is to fit ordinary least squares regressions per subject and correlate the resulting slope estimates directly. Under complete data, this slopes-first estimator is algebraically equivalent to a contrast-based plug-in estimator derived from the joint variance-covariance matrix of the two signals across time. Under independent dropout, however, the slopes-first approach implicitly applies heterogeneous weights across missing-data patterns, introducing bias whenever the cross-covariance between the two signals is not constant over time. We formalise this result and propose an unbiased alternative that estimates the joint covariance matrix before applying the contrast. We further extend our results to include covariates as well. Simulations confirm that the slopes-first estimator is biased under non-constant cross-covariance while the proposed alternative remains unbiased. Further, we show in a real-world example that the two estimators can lead to different scientific conclusions and recommend that researchers working with longitudinal data inspect the missing data pattern to make an informed decision about the estimator of choice.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.