Analytica Chimica Acta

Blood biomarker models are increasingly used in Alzheimer's disease and related dementia translational research, but predictive performance can be inflated when the same dataset is used for both model development and evaluation. We assess the effect of data double dipping using simulations and NULISA proteomic data from the MYHAT-NI community-based cohort to predict brain amyloid-beta neuroimaging status. In both settings, training AUC increased as more biomarkers were added, while testing AUC peaked earlier and then declined. These findings show that data double dipping can inflate model performance and highlight the need for external validation or internal validation with data partitioning.

Tuberculosis (TB) remains one of the deadliest infectious diseases, with over a million deaths annually and a growing threat from multidrug-resistant strains (MDR-TB). A major bottleneck in controlling TB is the lack of truly portable, rapid, and user-friendly diagnostic systems that can operate effectively in decentralized, resource-constrained settings. Here, we present a first-of-its-kind, portable nucleic-acid-based diagnostic platform that enables both primary TB screening and detection of drug resistance within the same unified framework, without any change in the operative embodiment. The system integrates loop-mediated isothermal amplification (LAMP) targeting dual Mycobacterium tuberculosis markers (IS6110 and IS1081) with a compact, AI-enabled device and smartphone-based readout, delivering rapid and reliable results at the point-of-care. Clinical evaluation across 105 samples demonstrated high sensitivity and specificity. Further validation through real-world deployment in a primary healthcare setting, using a single-gene (IS6110) configuration operated by minimally trained personnel, yielded 95.60% sensitivity and 100% specificity, benchmarked against GeneXpert. Critically, the same platform architecture, without modification, extends seamlessly to drug-resistance profiling, demonstrated here through a probe-free, allele-specific LAMP approach for identifying key mutations associated with rifampicin (rpoB) and isoniazid (katG) resistance. By combining robust molecular diagnostics with AI-driven automation in a compact and accessible format, this work represents a significant medical advancement toward democratizing TB care. The platform thus holds strong potential to enable early screening, guide timely treatment decisions, reduce transmission, and substantially strengthen global TB elimination efforts, particularly in high-burden, low-resource settings.

We present a compact pump-and-probe mid-infrared Optothermal Spectrometer (OTHES) equipped with Spatial Probing and Autocorrection (SPAC) optimized for robust intravital application in humans. SPAC-OTHES facilitates alignment stability and spectral comparability across different measurement sessions involving different skin types. Contrary to state-of-the-art, SPAC-OTHES uses camera-based beam detection and an auto-calibration mechanism that enables ca. 73% better spectral reproducibility in intravital measurements in human volunteers than non-calibrated readouts. Moreover, SPAC-OTHES has the potential to lower the glucose quantification error, as demonstrated here in artificial skin phantoms, where an improvement of 52% compared to conventional diode-based detection was observed. The compactness of OTHES, combined with reliable SPAC-readout, has the potential to accelerate commercialization and broad application of biosensors based on mid-infrared spectroscopy.

Background: The rising prices of cancer medicines have intensified concerns about treatment access and health system sustainability particularly in low- and middle-income settings. Systematic facility level evidence on what medicines is actually available, at what prices, and at what cost to patients remains scarce, constraining evidence-based policy reform. Methods: Using adapted WHO/Health action international methodology, we conducted a cross-sectional survey of 52 cancer medicines across five therapeutic classes at five health facilities in Kisumu County, Kenya. Availability was measured as the proportion of facilities stocking each medicine. Affordability was assessed using days' wages required for the lowest-paid government worker to purchase standard treatment regimens, calculated per one chemotherapy cycle and maximum possible cycles. Results: Overall medicine availability was 48.1%, with marked inter-facility variation. Affordability analysis revealed severe financial barriers. The breast cancer AC regimen required 19.6-47.4 days' wages per full course; cervical cancer cisplatin, 19.8-49.2 days' wages; colorectal FOLFOX, 80.0-303.6 days' wages; and prostate docetaxel reached 437 days' wages at the highest-cost facility. The Social Health Authority's (SHA) KES 550,000 annual ceiling adequately covered cytotoxic regimens for common cancers at competitive prices but was exceeded by 24-116% for HER2-positive breast cancer requiring trastuzumab, with further strain for recurrent cervical and metastatic prostate cancers. Conclusions: Cancer medicines in Kisumu County are inconsistently available and highly variable in price resulting in inequitable access. We call for urgent retail price markup regulation, expanded pooled procurement through KEMSA, inclusion of priority targeted therapies on the Kenya Essential Medicines List, and SHA benefit packages redesigned around full-course regimen costs.

Wearable digital health technologies may complement traditional gait assessments in amyotrophic lateral sclerosis (ALS) by sensitively capturing real-world mobility changes. In this study, we validated six digital gait metrics derived from ankle-worn sensors in a natural history cohort of 182 individuals with ALS. Investigated metrics correspond to various aspects of gait, including volume, speed, intensity, similarity, variability, and fragmentation. Longitudinal analyses showed significant declines in step count, peak cadence, stride intensity, and stride similarity, with increasing stride duration variability and walking fragmentation over 52 weeks. Many participants exhibited greater relative change in the gait metrics than the self-reported ALS Functional Rating Scale-Revised (ALSFRS-RSE). Stratified analyses revealed that digital metrics captured significant functional decline even in participants with stable walking scores on the ALSFRS-RSE. These findings support the potential utility of these metrics for disease monitoring in ALS clinical care and trials.

Background: Antenatal care (ANC) utilization is critical for improving maternal and neonatal health outcomes. Despite the World Health Organization recommendation of at least eight ANC contacts during pregnancy and the implementation of free maternal healthcare policies in Ghana, significant geographic and socioeconomic disparities in ANC utilization persist. This study therefore assessed the spatial distribution and geographically varying determinants of ANC utilization among women in Ghana. Methods: A cross sectional analytical study was conducted using women data from the 2022 Ghana Demographic and Health Survey. The analysis included women aged 15 to 49 years with an index child younger than five years preceding the survey. Descriptive statistics were computed using Stata version 18, while spatial analyses were conducted in QGIS version 3.44. Global Morans I was used to assess spatial autocorrelation, whereas Local Morans I and Getis Ord Gi analyses identified spatial clusters, hotspots, and coldspots of ANC utilization. Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR) models were fitted to assess global and local determinants of ANC utilization. Results: Overall, only 26.0% of women achieved adequate ANC utilization, while 74.0% reported inadequate ANC attendance. Adequate ANC utilization was higher among women with higher education (42.0%) and those from the richest households (41.3%) compared with women without formal education (19.1%) and those from the poorest households (17.6%). Regional disparities were observed, with Western (48.8%), Eastern (48.0%), and Greater Accra (47.3%) regions recording the highest ANC utilization, whereas Savannah (24.7%), Northern (25.8%), and North East (26.8%) regions recorded the lowest utilization levels. Global Morans I demonstrated significant positive spatial autocorrelation (Morans I = 0.457, p = 0.044), indicating geographic clustering of ANC utilization across Ghana. Getis Ord Gi analysis identified significant coldspots within Northern, Savannah, and North East regions, while Central Region demonstrated significant hotspot clustering. OLS regression showed that maternal education (B = 0.284, p = 0.003) and household wealth (B = 0.191, p = 0.011) positively influenced ANC utilization, whereas distance to health facility negatively influenced utilization (B = -0.156, p = 0.019). The GWR model demonstrated improved explanatory performance (Adjusted R-squared = 0.71), confirming substantial spatial heterogeneity in ANC determinants across Ghana. Conclusion: Adequate ANC utilization in Ghana remains low and geographically unequal. Maternal education, household wealth, and geographic accessibility significantly influence ANC utilization, with pronounced disparities concentrated within Northern Ghana. Spatially targeted maternal health interventions aimed at improving education, reducing socioeconomic inequalities, and enhancing healthcare accessibility are required to improve equitable ANC utilization across Ghana.

Background: Generative AI tools can support data-intensive research by writing code, drafting prose, searching analytical possibilities, and stress-testing claims. They can also produce false citations, drift between statistical specifications, and lose continuity across long investigations. This paper describes a practical workflow for using AI systems in empirical research while keeping discovery, verification, and accountability inspectable. Methods: We developed and applied a three-phase human-AI workflow to a case study of 14 elite Ethiopian distance runners. The dataset contained 22,605 GPS-segments collected across 97 consecutive days in late 2025, supplemented by venue and athlete metadata collected in the field. Phase 1 used an autonomous data-exploration tool to pre-filter the hypothesis space across five seeded research questions. Phase 2 used an AI system under direct human guidance to construct candidate findings into numerical claims, verification scripts, and draft text. Phase 3 used an independent AI system in an adversarial role to stress-test methods, statistics, prose, figures, and citations. The workflow was informed by Pearl's distinction between association, intervention, and counterfactual reasoning, with human judgement retained for research direction, interpretation, and final claims. Results: The workflow produced three empirical analyses and a documented correction process. The analyses estimated an altitude-to-sea-level pace correction of +0.10 min/km per 1,000 m at matched heart rate, showed why pooled altitude-surface regression was not identifiable within this venue system, documented method-dependence in heart-rate-based intensity classification, characterised within-venue route variation as a 64/36 path-fixed-to-trail-variable split with the Sululta label resolving into two functionally distinct sub-venues, and reframed the cohort's training through a 3x3x3 prescription lattice grounded in Ethiopian coaching practice. The adversarial phase identified several hallucinated citations, a terminology error between HC1 and cluster-robust standard errors, and several inconsistencies between prose, figures, and computed results. Verification scripts re-derived nearly all numerical claims from the cleaned lap-level data. Conclusions: The case study shows how researchers can organise AI-assisted empirical work so that candidate discovery, claim construction, independent stress-testing, and final accountability remain separated. The workflow did not remove the need for domain expertise or human judgement. Its value was in making the route from candidate finding to manuscript claim explicit, reproducible, and open to challenge. Trial registration: Not applicable.

Purpose: To identify, through iterative user-centered design, the auditory biofeedback requirements and sound preferences supporting gait training in children with cerebral palsy (CP), and to determine which feedback variables, sound mappings, and sound types yield clinically viable and movement-interpretable paradigms. Methods: The iterative process spanned two prototype phases. Prototype A comprised seven paradigms demonstrated to two experienced physiotherapists (Workshop 1A). Two of these were subsequently discarded owing to poor sound-movement interpretability and two were modified. Six paradigms were added to Prototype B, demonstrated to four children, five parents, and one therapist (Workshop 1B) and two therapists (Workshop 2B). Data were analyzed using systematic text condensation. Results: Within-child sound preferences varied with energy level and sensory state on a given day. Sound-movement interpretability tended to suffer for paradigms with greater acoustic complexity (e.g. computer-generated music). Therapists endorsed a repertoire spanning both movement quality and movement quantity targets. Participants independently proposed paradigms rewarding restrained and controlled movement, a feedback category absent from the current prototype. Conclusions: Session-level calibration is preferable to fixed sound profiles, requiring real-time interface support for paradigm adjustment. Acoustic complexity must remain subordinate to movement-sound interpretability. Paradigms targeting movement restraint are a development priority unaddressed in the literature.

Background: The specific 3D morphological substrates distinguishing the newly defined massive and torrential functional tricuspid regurgitation (FTR) phenotypes from standard severe disease remain under-characterized. Objectives: This study investigates the 3D geometric changes of the tricuspid valve (TV) apparatus across the spectrum of FTR, specifically focusing on the structural definition of massive and torrential grades. Methods: Three-dimensional (3D) transesophageal echocardiography (TEE) was performed in 322 patients with FTR secondary to left-sided heart disease. Patients were stratified into mild-moderate (n=166), severe (n=82), and massive-torrential (n=74) groups. TV geometry, including annular dimensions, leaflet tethering, and subvalvular apparatus, was quantified using 3D modeling software. Results: Patients with massive-torrential TR were characterized by advanced age, female predominance, and atrial fibrillation (75%). 3D analysis demonstrated that massive-torrential TR represents a distinct phenotype defined by extreme annular circularization (ellipticity index 1.0) and planar flattening (P < 0.001). Furthermore, these patients exhibited a critical leaflet-annulus uncoupling, where compensatory leaflet growth (relative length < 80%) failed to match the massive annular dilation. Consequently, the regurgitant orifice in massive-torrential grades appeared highly complex, frequently manifesting as multiple irregular orifices. Conclusions: Massive and torrential FTR are characterized by a unique geometric profile involving extreme annular circularization, severe leaflet tethering, and leaflet-annulus uncoupling. These morphological insights suggest that conventional repair strategies may be insufficient for these advanced phenotypes, highlighting the necessity for pre-procedural 3D TEE to guide device selection.

We assembled a multimodal clinical dataset describing demographics, placement history, prenatal substance exposure (PSE), birth characteristics, adverse childhood experiences (ACEs), International Classification of Diseases (ICD) diagnoses, and laboratory results for 3,685+ pediatric patients evaluated between 2014 and 2024 at the University of Minnesotas Adoption Medicine Clinic (AMC). Data were curated from electronic medical records through a combined manual and automated extraction protocol using a standardized operating procedure. The resulting dataset integrates structured EMR fields including neuropsychological, laboratory, and diagnostic information with manually pulled fields of ACE scores, PSE history, and placement history. We provide an overview of the population represented and describe the datasets structure, variable definitions, and validation procedures. This resource enables investigations into how early adversity impacts medical and developmental outcomes, and provides one of the largest standardized clinical placement history, PSE, and ACE datasets in an adoption and foster care pediatric population.

Aims. The risk of cognitive decline after losing a spouse remained mixed. This study aims to investigate the association between spousal loss and risk of cognitive decline, assess whether this association varies by sex and age, and identify modifiable factors. Methods. We conducted a prospective cohort study using harmonized data from six population-based aging surveys: the US Health and Retirement Study and its sister surveys in England, Mexico, China, India, and South Africa, incorporating their respective Harmonized Cognitive Assessment Protocol (HCAP) sub-studies. Spousal loss (yes vs no) was the exposure. Cognitive outcomes (i.e., orientation, memory, executive function, and language), were assessed using HCAP neuropsychological batteries. We conducted parallel analyses in six cohorts. Associations between spousal loss and cognitive outcomes were estimated using generalized linear models, and summarised estimates were derived via random-effects meta-analyses. Sex stratification and restricted cubic spines were used to examine how these associations vary by sex and age, respectively. Results. The analytical cohort consisted of 18,551 individuals aged 61.22 (SD 6.30) to 71.37 (SD 7.33) years. Widowhood prevalence ranged from 14.1% in CHARLS to 53.9% in HAALSI and was consistently higher in women. Spousal loss was associated with poorer memory (multivariable-adjusted {beta} = -0.07, 95% CI -0.12 to -0.01) and executive function (multivariable-adjusted {beta} = -0.08, 95% CI -0.13 to -0.03) in the meta-analysis, with no significant associations for orientation or language. While results were generally consistent in five cohorts, the ELSA showed divergent patterns (orientation: {beta} = 0.10, 95% CI 0.06 to 0.13; memory: {beta} = 0.05, 95% CI 0.02 to 0.08; language: {beta} = 0.16, 95% CI 0.12 to 0.19). Sex-stratified analyses indicated poorer executive function among men (multivariable-adjusted {beta} = -0.14, 95% CI -0.19 to -0.08) and poorer memory among women (multivariable-adjusted {beta} = -0.07, 95% CI -0.14 to -0.01) following widowhood. Nonlinear age-related effects on cognition were observed in ELSA, LASI, and HAALSI. Higher education, internet use, and BMI were negatively associated with the risk of cognitive decline among widowed participants. Conclusions. Spousal loss is associated with domain- and sex-specific differences in cognitive performance, with substantial heterogeneity across study populations. Future research should integrate biopsychosocial markers to develop context-sensitive interventions for widowed older adults.

COVID-19 risk scores developed during the pandemic relied on measurements contemporaneous with infection, leaving unresolved whether the metabolic and inflammatory vulnerability they capture pre-existed as a stable trait or was triggered by acute illness. Here, using 501,946 UK Biobank participants whose blood was drawn between 2006 and 2010---at least ten years before SARS-CoV-2 emerged---we show that baseline proteomic and metabolic profiles predict both COVID-19 hospitalization (2,783 events; C-statistic =0.676 [0.666--0.686]) and COVID-19 mortality (1,564 deaths; C-statistic =0.730 [0.701--0.760]) from parsimonious, regularized feature sets. The IL-1 pathway index (xIL1, +0.093) was independently selected for hospitalization but not mortality, while the IL-6 trans-signaling index (xIL6, + 0.040) was selected for mortality but not hospitalization---a differential pathway weighting corroborated by independent LightGBM/SHAP analysis and mirroring the subsequent success of tocilizumab (anti-IL-6R) and the limited efficacy of anakinra (anti-IL-1R) in reducing COVID-19 mortality in randomized trials conducted years later. The mortality model was additionally characterized by central adiposity (waist-hip ratio, +0.386), a respiratory compromise index (xRSP, +0.149), and prodromal cardiovascular disease (pCVD, +0.246). These findings establish that vulnerability to a novel pathogen is, in substantial part, a pre-existing and measurable prodromal state, with implications for pandemic preparedness and population-level risk stratification.

Maternal infection, immune disease, and delivery mode are plausible influences on early brain development. We analyzed 1,179,611 US Merative MarketScan mother-child pairs (2003-2024), including 259,339 non-twin siblings in 123,926 families. Population models screened 18 perinatal exposures against 13 childhood psychiatric/neurodevelopmental diagnosis-count outcomes; sibling fixed effects tested robustness to stable family-level confounding. Cesarean delivery was associated with higher composite neurodevelopmental diagnosis counts in pairs (23.4%) and siblings (25.0%) and with ADHD in siblings (38.8%; FDR q = 0.025). Autism was elevated in pairs (20.0%) but not supported within families (5.0%; p = 0.87). Claims-defined no-labor/no-repeat cesarean showed stronger lower-risk-birth associations for composite neurodevelopmental burden (48.0%), autism (44.9%), speech/language disorders (41.0%), and ADHD (24.1%). Maternal infection/immune-mediated disease, preterm birth, and advanced maternal age were additional population signals.

Background. Cross-validation (CV) is widely used to estimate predictive performance, but can overestimate performance when applied at the observation level to repeated-measures data. When continuous predictor variables are measured repeatedly within subjects and the binary outcome is defined at the subject level, naive observation-level CV introduces data leakage through within-subject dependence, producing optimistically biased estimates of the area under the receiver operating characteristic curve (AUROC). The magnitude of this bias and the performance of alternative partitioning strategies have not been formally characterized for this data structure. Methods. Three CV strategies were compared for estimating subject-level AUROC in ridge logistic regression models: naive observation-level 10-fold CV, subject-level 10-fold CV, and leave-one-cluster-out (LOCO) CV. The framework was applied to a motivating clinical dataset of daily oxygenation measures and retinopathy of prematurity outcomes among 101 extremely low birth weight infants. A factorial simulation study was conducted across 162 parameter combinations varying cluster count (20-150), intraclass correlation (0.1-0.5), within-cluster autocorrelation (0.2-0.8), and outcome prevalence (10-35%), with 500 simulated datasets per condition (76,389 valid datasets total). Results. In the motivating dataset, naive CV produced optimism of +0.078 AUROC units for severe ROP prediction (15 events, 101 subjects) and +0.031 for any ROP prediction (48 events). Subject-level 10-fold CV closely approximated LOCO (deviation [&le;] 0.015). In the simulation, naive CV optimism ranged from +0.039 to +0.204 across all conditions, increasing monotonically with higher ICC, higher autocorrelation, fewer clusters, and lower event rates. Subject-level 10-fold CV was essentially unbiased relative to LOCO across all 162 conditions (mean absolute deviation = 0.002). Conclusions. Naive observation-level CV meaningfully overestimates discriminative performance in the repeated-measures binary outcome setting and should not be used. Subject-level CV partitioning effectively eliminates this bias. Accordingly, subject-level partitioning should be considered essential, not optional, when validating prediction models using repeated-measures data with subject-level outcomes.

Background: It is well-established that males have a higher mortality risk than females. Immune cells and their function are known to undergo characteristic changes during aging, and immune cells are known to have sex differences. Immune cells and their function have been linked to mortality risk, but no studies have investigated to what degree, if at all, Immune Cell Biomarkers (ICBs) contribute to the known differences in mortality risk by sex. Methods: Using participant data from the Health and Retirement Study (n = 8,822), we applied multivariable linear regressions adjusting for age, cytomegalovirus (CMV) serostatus, sex, and race/ethnicity to identify differences by sex in 48 immune cell biomarker (ICB, e.g. T cells, B cells, Monocytes, etc.) percentages and counts (measured in 2016). We studied how the associations between ICBs and mortality risk differ by sex using stratified Cox Proportional Hazard (CPH) models. We estimated how inclusion of sex explained the relationship between ICBs and all-cause mortality, and conversely, how inclusion of individual and all ICBs combined explain the relationship between sex and all-cause mortality using multivariable modeling approaches. Results: Differences in ICBs by sex range between 2-38% (39/48 statistically significant). 9 ICBs were significantly associated with mortality risk in the entire sample. While different ICBs were significantly associated with mortality risk in the stratified analyses, particularly with respect to monocyte, B cell, and NK cell populations, adjusting for sex modestly influenced the hazard ratios of the ICBs (sex: 8 ICBs, percent change <5.4%). Furthermore, individual and cumulative contributions of ICBs in explaining the differences in mortality risk by sex were not significant.

Background: People with ischemic heart disease (IHD) remain at high risk of recurrent major cardiovascular events despite contemporary therapy. Over two decades, a translational research program has evaluated pressure pain sensitivity (PPS) as a non-invasive marker of central autonomic dysfunction and a mutual risk phenotype in IHD and type 2 diabetes. A PPS-guided non-pharmacological intervention has been shown to substantially reduce five-year all-cause mortality in IHD. Methods: In a randomized controlled trial, 213 adults with stable IHD and elevated PPS, suggesting ANSD, were allocated to PPS-guided intervention (n=106) or control (n=107). The active group received three months of structured education (daily PPS self-measurement, cutaneous sensory nerve stimulation, supportive mental and physical exercises, telemedical feedback) followed by self-directed continuation. Controls received a booklet on general stress-management. The primary endpoint for this prespecified secondary analysis was a composite of eight major cardiovascular events. Results: Over 5 years, at least one major adverse cardiovascular event occurred in 19.8% of the PPS-guided group versus 43.8% of controls (odds ratio 0.32, 95% CI 0.17-0.62, P=0.0003). Incidence rates were directionally in favor of active intervention across all event categories (P=0.004). Conclusions: A brief PPS-guided non-pharmacological intervention, followed by self-directed continuation, was associated with a marked long-term reduction in major adverse cardiovascular events, complementing previously reported large reductions in all-cause mortality in the same cohort. Within the context of a multi-decade PPS research program, these findings support PPS-guided care as a low-resource autonomic intervention ready for pragmatic scale-up testing as an adjunct to cardiometabolic care.

Background and Aims: The North American overdose crisis is increasingly characterized by complex polysubstance use alongside a transition from injecting to smoking unregulated opioids. However, transitions involving multiple substances remain understudied. We characterized longitudinal transitions in the route of administration and frequency of heroin, fentanyl, and methamphetamine use and examined whether these transitions differed by multilevel factors hypothesized to influence patterns of polysubstance use and routes of administration over time. Design: People who inject drugs (PWID) enrolled in a cohort study completed baseline surveys (October 2020-2021) and three biannual follow-up visits (through April 2023). Setting: San Diego, California, and Tijuana, Baja California. Participants: Among 612 PWID, median age was 43 years; most were male (74%), Hispanic, Latino, or Mexican (72%), and San Diego residents (67%). Measurements: Based on past six-month substance use behaviors reported at each visit, we categorized participants according to six indicators over time: low- (< weekly) and high-frequency ([&ge;] weekly) smoking and injecting of heroin, fentanyl, and methamphetamine. We then used latent transition analysis (LTA) to identify distinct subgroups of participants with respect to these indicators at baseline and examine transitions between them over 18 months. We fit models with 2-5 subgroups, selecting the final model based on fit and interpretability and used multiple-groups LTA to examine differences in subgroup transitions by multilevel factors. Findings: We identified four subgroups: Subgroup 1 (Heroin-Methamphetamine Polyroute), characterized by high-frequency heroin and methamphetamine smoking and injection, included 22% of participants at baseline but 0% at 18 months. Subgroup 2 (Methamphetamine-dominant Smoking), characterized by high-frequency methamphetamine smoking, accounted for 14% of participants at baseline and 18 months. Subgroup 3 (Fentanyl-Methamphetamine Smoking), characterized by high-frequency fentanyl and methamphetamine smoking, included 4% of participants at baseline and 21% at 18 months. Subgroup 4 (Heroin-dominant Injecting), characterized by high-frequency heroin injection, included 61% of participants at baseline and 65% at 18 months. Participants in Subgroup 1 primarily transitioned to Subgroups 3 and 4 over time. Larger increases in Subgroup 3 prevalence occurred for participants who, at baseline, experienced homelessness, resided in San Diego (vs. Tijuana), received syringes from a syringe services program, and overdosed in the past six months. Conclusions: PWID in this region increasingly transitioned from high-frequency heroin and methamphetamine injection toward fentanyl and methamphetamine smoking, likely reflecting shifts in drug availability. Results highlight the need for multilevel interventions that address health harms resulting from polysubstance smoking alongside continued injection.

Background: Accurate early identification of sepsis remains a major clinical challenge due to its heterogeneous presentation and overlap of clinical signs with the non-infectious systemic inflammatory response syndrome (SIRS). Timely differentiation is crucial for improving patient outcomes, meeting sepsis bundle requirements and reducing inappropriate antimicrobial use. We hypothesized that clinical and laboratory data available within the first 3 hours of patient presentation could be used to identify patients with sepsis to an actionable level of accuracy, in lieu of traditional microbiology results which would not become available until at least 12-24 hours. Data from two independent studies were used to quantify the diagnostic value of demographic, vital, clinical-laboratory, and microbiological data available at three time points for distinguishing retrospectively diagnosed critically ill patients with either sepsis or non-infectious SIRS. A particular focus of this work was an assessment of the utility of SeptiCyte RAPID (Immunexpress Inc., Seattle, Washington, USA) as an aid to sepsis diagnosis, producing actionable data within 1 hour. Methods: Data from two independent study cohorts were analysed. The 510k cohort consisted of 419 adult patients in intensive care (ICU) (MARS, VENUS, and NEPTUNE trials). The Andalusian cohort consisted of 353 ICU patients from the PANGEA study. Logistic regression models, selected by a greedy search algorithm and validated by repeated cross-validation, were used to determine the contributions of different variables to diagnostic accuracy. Diagnostic performance was quantified by area under the receiver operating characteristic curve (AUC). Results: For the 510k cohort, a baseline AUC of 0.69-0.73 was observed using 5-7 vital and demographic variables assessed immediately upon presentation (time T1). The addition of clinical-laboratory variables, in particular SeptiCyte RAPID, within 1-3 hours post-presentation (time T2) increased the AUC to 0.83-0.85). Finally, the addition of microbiological data 12-24 hours post-presentation (time T3) further improved the AUC to 0.90-0.91. Similar results were obtained for the Andalusian cohort. AUC values at the three time points were as follows: At time T1, AUC = 0.67 based solely on vital signs and demographics; at time T2, AUC = 0.87 based on vitals + demographics + SeptiCyte RAPID or other clinical laboratory data; at time T3, AUC = 0.93 based on vitals + demographics + SeptiCyte RAPID or other clinical laboratory data + microbiology results). For both cohorts, the most significant variables included temperature, mean arterial pressure, respiratory rate, suspected infection site; SeptiCyte RAPID, procalcitonin, confirmed bacterial infection and positive blood culture confirmation. Conclusions: Accuracy of identification of sepsis increases markedly as demographics and vital signs are supplemented with clinical-laboratory information, and ultimately with microbiological culture results. The fastest improvement occurs within the first 3 hours when laboratory data, and in particular SeptiCyte RAPID results, become available. Integrating rapid host-response testing with SeptiCyte RAPID into time-based diagnostic frameworks may enhance early sepsis recognition, improve antimicrobial stewardship, and support guideline-driven clinical decisions.

A closed-loop quality system deployed across thirteen US hospital sites resolved physician complaints with zero regressions on 42 tracked cases across 1,089 optimization iterations, while a deterministic assembly-agent replacement cut H+P trace latency from 19.6 s to 10.8 s (-8.8 s, 95% CI [-10.5, -7.1] s; n = 100 pre, n = 100 post). We report four observations and an architectural follow-through. First, the same binary-check instrument produces opposite outcomes depending on the question asked: "maximize this score" produces structurally-correct notes that physicians reject (Spearman rho = -0.077, 95% CI [-0.40, 0.26], n = 36); "did this specific fabrication stop?" produces rater-invariant deployment decisions. Second, in our pipeline, assembly-stage agents did not respond to prompt optimization the way reasoning agents did: four consecutive optimization attempts produced 18-28 point regressions. Third, physician preference is rater-fragile at typical clinical-AI calibration sample sizes (Cohen's kappa = 0.028 between two board-certified physicians, 95% CI [-0.30, 0.36] on n = 35 overlapping pairs). Fourth, the architectural punchline: six weeks after the prediction, the LLM call at the chart-assembly step was replaced with a deterministic renderer (sub-500-character template plus sandboxed scripting), lifting the defect-free rate on a 51-case holdout from 49% to 84%. We introduce a Pareto-with-absolute-floors acceptance rule (multi-axis commit with severity-class categorical vetoes) as a methodological contribution distinct from scalar-reward acceptance in standard prompt-optimization frameworks. Cross-iteration rejection memory prevents the loop from re-proposing edits already rejected three or more times. A reproducibility bundle (anonymized ablation per-case counts, bootstrap-CI data, analysis scripts) is released under CC BY 4.0 at github.com/sayvant/SQS-Auditor-paper-data.

The Asian population in Los Angeles is among the largest and most heterogeneous in the U.S. This is true culturally and health-wise. Older Asians have differing risks for cardiovascular and cardiometabolic disease, depending on their ethnicity, health literacy, and lifestyle choices. This pilot examines several of these factors in a small but diverse group of older Asian adults who attended community health events from 2024-2025. Self-reported and biometric data were collected at five such events hosted by the Asian Pacific Health Corps at UCLA. The pilot generated health literacy and lifestyle (HLL) scores for all participating attendees and explored how they relate to their socio-demographics, healthcare habits, and predictions of their own health data. Overall, there were significantly more females than males with higher HLL scores (p = 0.027). College education (p = 0.028) and "normal" ranges for biometric data (e.g., blood pressure, BMI, blood glucose, cholesterol) were related to higher median HLL scores. With a few exceptions, fewer than 50% accurately predicted their biometric numbers regardless of HLL scores, suggesting a disconnect between perception and reality, and that better provider-patient communication may help foster greater patient understanding about their chronic conditions. These HLL score distributions indicate that educational attainment, better awareness of one's health, and high health literacy are individual factors that may influence older Asians' understanding and potential approach to managing their health conditions.