Frontiers in Systems Neuroscience

Integrating multisensory and behavioral information is essential for sensory perception. In the auditory system, multisensory and behavioral influences emerge early in subcortical structures. Descending projections from non-auditory cortical areas are well positioned to convey such signals, yet how they shape subcortical auditory processing remains poorly understood. Here, we investigated corticocollicular projections from the primary somatosensory (S1) and motor (M1) cortices to the inferior colliculus (IC), a principal integration center in the auditory midbrain. We found that trunk- and limb-related regions of S1 and M1 form prominent monosynaptic projections to the IC, and that optogenetic activation of these projections robustly drives IC activity. Notably, a substantial population of cortical-responsive neurons did not respond to sound. In sound-responsive neurons, concurrent cortical stimulation enhanced sound-evoked responses, whereas cortical activation preceding sound onset suppressed them. Furthermore, both cortical-responsive IC neurons and deep-layer S1 and M1 neurons exhibited locomotion-related modulation and anticipatory activity prior to movement onset, suggesting that these descending pathways convey movement-related signals to the IC. Together, our findings identify a descending sensorimotor circuit that integrates body- and movement-related information with auditory processing in the auditory midbrain.

During wakefulness, we are used to perceive the environment through our senses, act on it and take these inputs to update our experiences and build the perceptions. When the inputs are not longer accurate or structured, people would sometimes have hallucinatory experiences. Whether such experiences are associated with distinct patterns of thought, and how they relate to large scale brain dynamics, remains unclear. To address these questions, we combined experience sampling protocol with EEG recording during multimodal Ganzfeld, where participants were exposed to unstructured, uniform visual and auditory stimulation. Participants repeatedly reported the complexity of their visual experiences together with ongoing thoughts related to perceptual belief, prediction perception mismatch, active updating, and prior mentation. EEG microstates were extracted to characterize the temporal dynamics of large-scale brain networks. We found that visual complexity was related to all four dimensions, but partly distinct in simple and complex visual experiences. These phenomenological changes were accompanied by distinct, and often nonlinear, dynamics of large-scale brain networks involved in visual processing, salience detection, and internally directed cognition. It also indicates that this paradigm might be a valuable model for investigating the mechanisms underlying hallucinatory experiences in psychosis.

Past studies using transcranial magnetic stimulation have shown larger motor-evoked potentials when people observe someone lifting a heavy object than when they observe someone lifting a light one. This means that observers may engage their own motor system in proportion to the perceived effort. However, the different responses during the observation of light and heavy objects may have been influenced by predictable trial sequences within blocked presentation, making it unclear whether corticospinal excitability reflects online processing of kinematics or is affected by top-down expectations. In this Registered Report, 57 right-handed participants passively observed videos of a precision grip and lift of heavy and light objects while receiving a single-pulse TMS to the left primary motor cortex during the lift phase of the movement. Motor-evoked potentials were recorded from the right first dorsal interosseous muscle. The study compared two main observation contexts: a predictable trial sequence in which repeated videos of the same lifts were presented in a blocked order, and an unpredictable one in which videos were presented semi-randomly and participants could rely only on kinematic cues to perceive the weight of the lifted object. In both conditions, the same videos of lifts of equivalent-looking heavy and light objects were used and only the order of presentation differed. Contrary to our predictions, in the blocked (predictable) condition, there was no significant difference in MEPs elicited by light and heavy lifts. In the unpredictable condition, participants showed greater corticospinal excitability during the observation of the light lifts compared to the heavy lifts. This suggests that in the absence of predictable information, the corticospinal system was sensitive to the observed kinematics, but contrary to previous findings, its excitability varied inversely with the object weight.

A new electrical stimulation focused computational model of the visual cortex had been created to aid in the development of cortical visual prosthesis. The model consists of 10,666 biophysical neurons representing 0.13mm3 of a layer 2/3 of the primary visual cortex and was calibrated to match the baseline activity of rat brain recordings. A novel model of electrical stimulation was developed to allow for selective activation of specific neuron types, and matched the single cell stimulation response generated by known stimulation models. The electrode was tuned to match recorded population level change in activity across distances and currents recorded in the rats brain. The model is now ready to explore electrical stimulation effects on the visual cortex for examination of neuron specific stimulation to assist in the development of cortical visual prosthesis.

Introduction: Decision making for selecting an object or a course of action from possible alternatives largely depends on our perceptual ability modulated by attention. When multiple stimuli appear close together in time, processing one stimulus can temporarily impair the processing of another due to temporal limitations of attention. Observers frequently fail to detect the second target (T2) presented within a few hundred milliseconds after the first target (T1) in a stream of stimuli, which is commonly known as attentional blink (AB). Existing theories attribute this perceptual lapse to T1 processing, distractor interference, or transient attentional gating; however, the computations underlying suppressive mechanism remains unresolved. We investigated whether pupil-size could reveal the underlying mechanisms of AB and predict conscious perception on a trial-by-trial basis. Methods: Pupil diameter and gaze locations were recorded using an infrared eye tracker. Machine learning techniques were used to classify trials when T2 was detected versus when it was not, after correct identification of T1, during an AB task from the pupil dynamics, which also yielded attentional episode (AE) associated with each element in the stream of visual stimuli when deconvolved. Results: Cross-validating classifiers achieved near-perfect accuracy not only in distinguishing but also predicting perceptual outcomes on a single-trial basis. AEs exhibited greater power when T2 was detected than when it was missed; the differential power in AEs on a logarithmic scale was highly synced with the differential pupil size. Conclusions: Collectively, these findings establish a framework for predicting attention-driven perceptual outcomes from pupil-dynamics at finer time-scale.

Alarm pheromone is a high-priority social signal in honey bees, yet direct evidence for how its major component, isopentyl acetate (IPA), is encoded in antennal lobe remains limited. Here, we combine intracellular recording, neuronal staining, and three-dimensional reconstruction to examine neural responses to IPA in the honey bee brain. Integrated analysis of antennal lobe neurons revealed clear but heterogeneous time-locked responses to IPA, which could be grouped into four temporal response motifs: fast transient, monophasic, biphasic excitation-inhibition, and delayed excitation-inhibition. A morphologically identified antennal lobe neuron exhibited a stable excitatory response characterized by short latency and prolonged elevated firing after stimulus onset. In a representative delayed-type antennal lobe neuron, response magnitude showed strong concentration dependence: peak amplitude and post-peak inhibition increased significantly with increasing IPA concentration, whereas peak latency remained largely unchanged. Repeated stimulation at an intermediate concentration produced comparatively modest effects, expressed mainly as attenuation of peak amplitude and a gradual delay in response timing. In addition to antennal lobe neurons, we identified two IPA-responsive protocerebral neurons. Together, these results provide direct single-neuron evidence that IPA is heterogeneously encoded in the honey bee antennal lobe.

Tactile imagery has attracted growing fundamental and clinical interest. Previous studies often investigated neural and functional similarities between imagined and actual touch. Several functional aspects of touch, such as differences between active and passive touch, between different haptic features during active touch or sensitivity of different body parts for passive touch, have also been explored in tactile imagery. Furthermore, considerable individual differences in the ability to engage in tactile imagery have been observed. However, several important aspects, involving different imagery components and a wide variety of touch qualities remain to be explored within a single comprehensive study. The current study therefore aims to provide a wide-ranging assessment of tactile imagery in terms of imagery processing components (vividness, maintenance, transformation), type of touch (active versus passive) and touch qualities (object properties for active touch, different tactile sensations across body sites for passive touch). We developed a comprehensive questionnaire containing 72 items to assess tactile imagery ability. 136 healthy participants were asked to imagine different touch types and rate imagery vividness and their ability to maintain and transform each sensation on 5-point Likert-scales. Active touch varied by object (plastic bottle, modeling clay, sponge) and property (temperature, weight, texture, resistance). Passive touch varied by body site (lip, shin, sole of the foot, lower back) and sensation (stroking, vibration, pinching). Overall, participants were able to perform tactile imagery: the vast majority reported at least some imagery across touch types. Individual variability was substantial: scores bridged both ends of the scale. Active tactile imagery differed significantly between objects, depending on tactile property. Object-property pairs with particularly strong imagery were bottle-temperature, bottle-weight and sponge-texture, whereas bottle-resistance elicited weaker imagery, as did temperature and weight for both sponge and clay. Passive tactile imagery was significantly stronger for body sites with higher receptor density (i.e. lip and foot). Imagery of stroking was significantly weaker than vibration and pinching. Active and passive imagery showed a strong, positive correlation, though some participants had relatively strong active imagery, but weaker passive imagery, or vice versa. Our findings confirm that tactile imagery ability varies across individuals and touch types, underlining the importance of a comprehensive imagery ability assessment tool specific to the tactile domain.

Investigating the neural mechanisms of social group interactions and other naturalistic behaviors in small animals remains limited by current technology. Tethered neural recording systems are incompatible with many of these behaviors, while existing wireless devices for small animals are constrained by weight, bandwidth, recording duration, and the lack of closed-loop modulation capabilities. To overcome these limitations, we developed a Wireless, Interactive, Lightweight Datalogger (WILD) with integrated flexible neural probes, optogenetics, an inertial measurement unit, an ultrasonic microphone, and a head-mounted camera. This platform enables simultaneous, long-term recording of neural activity, locomotor variables, vocalizations, and eye movements from groups of freely moving mice in both laboratory and outdoor settings. Model-based real-time signal processing detects specific neural events and behavioral motifs to trigger closed-loop neural interventions. By combining multimodal recordings with advanced onboard signal-processing capabilities in a compact device, WILD enables the investigation of neural mechanisms underlying a broad range of natural behaviors in small animals.

Objectives: To estimate the prevalence of Post-Exertional Malaise (PEM) among adults with prior COVID-19 and associated mental health and disability outcomes. Methods: We conducted a cross-sectional analysis of data from a survey of 9,620 adults with prior COVID-19 in New York City, collected May - June 2024. PEM was measured with the DePaul Symptom Questionnaire - Post Exertional Malaise, categorized by symptom duration (< 14 vs. [&ge;]14 hours). Weighted prevalence estimates were stratified by socio-demographic and clinical characteristics. Modified Poisson regression was used to assess the association of PEM with depression, anxiety, and disability. Results: The prevalence of PEM symptoms was 20.9% overall and 4.0% with symptom duration [&ge;]14 hours, representing over 800,000 New Yorkers affected and over 150,000 who meet a diagnostic criterion for ME/CFS. PEM prevalence was higher among women, transgender and non-binary adults, people of color, and lower educational attainment, chronic comorbidities, or disabilities. PEM was associated with 3 - 4 times higher prevalence of mental health outcomes and 4 - 5 times higher disability scores. Conclusions: PEM symptoms were common and strongly associated with disability and adverse mental health. Screening, pathways to care, and supportive policies are needed to mitigate long-term consequences, particularly among marginalized populations.

Obesity is a growing public health challenge, with body mass index (BMI) influenced by both genetic and environmental factors. While the role of direct genetic transmission is well established, evidence for genetic nurture effects, in which parental genotypes impact offspring through the environment, has remained mixed. This study investigates direct genetic transmission and genetic nurture effects on BMI across ages, using parent-offspring trios and pairs from the Dutch Lifelines cohort study (N = 18,897 offspring, aged 8 to 67 years). We leveraged the latest multi-ancestry BMI polygenic score (PGS) to construct transmitted (PGS-T) and non-transmitted (PGS-NT) polygenic scores, where PGS-NT consists of parental alleles not passed on to offspring and serves as a proxy for genetic nurture. Linear mixed models showed a large effect of PGS-T on offspring BMI (Beta = 0.416, p < 0.001), corresponding to a 1.85 kg/m2 increase per SD increase in PGS-T. PGS-NT had a small but significant effect (Beta = 0.026, p = 0.013), consistent with a genetic nurture effect accounting for approximately 6.6% of the effect of direct transmission. Parent-of-origin analyses showed that maternal PGS-NT effects were larger than paternal effects. PGS-T interactions with age indicated that direct transmission effects increased in childhood and stabilized in adulthood, while PGS-NT effects remained stable across age. Our findings suggest that direct genetic transmission is the dominant influence on BMI, while results are consistent with small genetic nurture effects that are driven by the maternal side.

Micronutrient deficiencies are prevalent in Madagascar, where diets rely heavily on starchy staples and access to animal-source foods is limited. Small dried fish (SDF) are widely available, yet their nutritional value and health risks remain poorly documented. We combined market surveys, taxonomic identification, and micronutrient and heavy metal analyses of nine SDF types collected along National Road 7. The samples encompassed 33 fish families, were dominated by small pelagic species (Clupeidae and Engraulidae), and were appreciated by consumers. A daily portion (5 g for infants; 10 g for young children and women of childbearing age) contributed substantially to Recommended Nutrient Intakes (RNIs). Across samples and groups, SDF were rich (>30% of RNI) in selenium and, for infants and young children, in calcium. All samples were a source of (>15% of RNI), or rich in, phosphorus, whereas iron contributions were more variable but often substantial. Several samples exceeded 100% of RNIs for selenium, calcium, iron, or manganese in infants and young children, and some were also sources of magnesium and, less frequently, zinc. Vitamin A was absent from sun-dried samples but detected in a smoked freshwater type. Heavy metal concentrations varied markedly, and portions of several types led to estimated exposures to inorganic arsenic or cadmium exceeding reference values, whereas freshwater species and some pelagic types showed a more favorable nutrition-risk balance. Overall, SDF are affordable, nutrient-dense foods with strong potential to alleviate micronutrient deficiencies in Madagascar, while highlighting the need for type-specific guidance to balance nutritional benefits and contamination risks.

We propose two new tests to detect drug effects (DE) in trials of one to very few patients followed during two periods (before and after initiation of a treatment). Both methods use longitudinal natural history data to inform the estimation of each patient's DE. The first method uses a non linear mixed effect model (NLMEM) reflecting an expected natural history with a hypothetical drug effect, to estimate the Conditional Distribution of the Drug Effect (CDDE). The second method trains a Pareto Depth Analysis (PDA) algorithm, a machine learning based approach based on outlier detection, that we implement using data simulated under the NLMEM. We evaluated the two tests with a simulation study. We used data from the PROSPAX study in Autosomal Recessive Cerebellar Ataxias (ARCAs, to derive a NLMEM for the Scale for the Assessment and Rating of Ataxia score. The CDDE method provided controlled type I error and, in some scenarios, adequate corrected power, though sensitivity analyses showed vulnerability to misspecification. The PDA method demonstrated lower statistical power except with high score precision. These results highlight different strategies for quantifying treatment effects in ultra rare, patient' specific trials. They can inform methodological design for future ARCA precision therapies.

Abstract Background: Vaginal steaming has gained popularity among women for reasons known best to them. However, the effects of vaginal steaming on vaginal lactobacilli levels remain poorly understood and understudied. This study investigated the prevalence, assessed differences in the presence of bacterial vaginosis (BV) among women who practiced vaginal steaming and those who do not and determined the factors associated with vaginal steaming among women attending Mulago Sexually Transmitted Infections (STI) clinic in Uganda. Methods: This study utilized a cross-sectional design to enroll 181 women aged 18 to 49 years who were systematically sampled at Mulago STI clinic. Interviews were conducted to obtain the demographic characteristics of the participants. Vaginal swabbing and Gram staining were done to attain lactobacilli counts by microscopy which were categorized using the Nugent score. Data were analyzed using Stata. The potential confounding effects of other variables on the relation between vaginal steaming and the presence of bacterial vaginosis as well as factors associated with vaginal steaming were assessed using modified Poisson regression. Results: Prevalence of vaginal steaming was 40.3%, (95% confidence interval (CI) 33.0% - 48.0%). There were 41.1% women who practiced vaginal steaming occasionally, 53.4% who used hot water having herbs and 78.1% who practiced vaginal steaming for medical reasons. There was no difference in the presence of bacterial vaginosis when women who practiced vaginal steaming were compared to those who did not (p-value = 0.286). Factors that were significantly associated with vaginal steaming included having experienced vaginal issues (aPR = 0.07, 95% CI 0.01 - 0.12, p value = < 0.001) and contraceptive use (aPR= 0.52, 95% CI 0.37 - 0.72, p value = 0.001). Conclusions: About 2 in every 5 women at Mulago STI clinic reported to have indulged in vaginal steaming. There was no difference in the presence of bacterial vaginosis when women who practiced vaginal steaming were compared to those who did not. Having experienced vaginal issues and contraceptive use were significantly associated with vaginal steaming among women at Mulago STI clinic, Uganda. The Ministry of Health of Uganda should establish targeted screening and treatment for bacterial vaginosis alongside other sexually transmitted infections.

Improvements in data availability, sharing, and integration, together with the development of explainable artificial intelligence (XAI) techniques, are advancing precision medicine for pediatric cancer by facilitating diagnosis, biomarker discovery, and drug development. Data sharing commons and initiatives like the Childhood Cancer Data Initiative (CCDI) provide access to pediatric-specific genomic and clinical data cohorts and improve data availability for pediatric cancer research. Based on CCDI, a scalable AI platform, Graph Artificial Intelligence for Pediatric Oncology (GAIPO), integrates various data modalities from bulk and single-cell omics data to clinical information. Such multi-modal data facilitates the training and development of advanced XAI models for pediatric cancers. We then developed an end-to-end multi-modality framework, PCGS, for pediatric cancer by incorporating omics-specific representation learning via GNN models with cross-attention fusion and multi-objective learning for downstream tasks such as classification, clustering, and survival analysis. This framework outperforms previous supervised multi-omics integration baseline approaches based on glioma and Wilms tumor cohorts and enables GNN model explainability via Shapley value-based feature attribution approaches to explain the contributions of gene-level features across various biomedical tasks, including classification and survival. Given specific background samples (e.g., age groups, sex, grades) as baselines, this explainable GNN model estimates and ranks the importance scores for input features from each omics modality. It identifies background-specific key features for biomarker discovery, risk group identification, and survival analysis in glioma and Wilms tumor, with potential applicability to other pediatric cancers.

Background. Routine service databases are attractive sources of training labels for clinical prediction models, but the processes that write those labels are rarely audited before the labels are used. In a deployed community cognitive-screening programme, we audited the routine cognitive-status label, built a matrix of twenty-four model arms over the same patients under a specialist reference standard, and measured what each supervision choice bought or cost. Methods. The study cohort is the 672 individuals whose cognitive status was recorded by a titled (attending-or-above) physician, that record being the reference standard; after holding out one institution entirely, a development panel of 642 individuals at 38 institutions. The routine cognitive-status label these individuals also carry was first audited at the operator level: for each data-entry account we counted diagnoses entered and the proportion recording any impairment, and tested a competing bulk-timestamp explanation. Twenty-four arms span the supervision choices such a programme faces: an incumbent 21-variable logistic regression; local language models (Qwen2.5-1.5B/3B, Qwen3-4B/8B) zero-shot, with chain-of-thought, fine-tuned on physician labels, on routine labels with and without decontamination, or on a proxy scale-band task; preference-optimised (DPO) and reinforcement-trained (GRPO) variants; a proprietary frontier model queried zero-shot; and knowledge distillation of that frontier model into the regression and into the local 4B, using 943 teacher-labelled records from the programme's unlabelled pool. All arms are scored out-of-fold under one five-fold split grouped on registry-resolved institution clusters (no cluster spans a fold); paired contrasts use a 2,000-draw cluster bootstrap. Results. 181 operator accounts (each entering at least 100 diagnoses with zero recorded impairments) account for 45,315 rows - 40.5% of the outcome column; recorded impairment falls monotonically with account volume (15.7% for 1-9 rows to 0.7% for 500-999); a bulk-timestamp explanation was tested and refuted, identifying the write-time column as a migration artefact. Under the specialist standard, no locally fine-tuned arm beat the incumbent regression (AUROC 0.926): physician-label SFT reached 0.924 (4B), DPO 0.881, and GRPO 0.789; the pre-registered two-stage proxy-then-RL recipe was worse than its single-stage contaminated baseline (-0.030, 95% CI -0.077 to -0.004). Chain-of-thought reduced discrimination at every size (-0.072, -0.080, -0.041 at 1.5B/3B/4B; -0.012, n.s., at 8B). The frontier model scored 0.932 (vs. regression +0.007, n.s.). The distilled 4B reached 0.940 - above the incumbent (+0.014, 0.004 to 0.031) and above its own teacher (+0.008, 0.001 to 0.017) - with near-teacher calibration; it reached the teacher's level by 50 teacher labels and changed little beyond 200. Conclusions. The audit and the arm matrix support one deployment recipe: audit the routine label at the operator level before training on it; do not expect fine-tuning, preference optimisation, or reinforcement learning on a few hundred specialist cases to beat a well-calibrated regression; and if a frontier model is available but undeployable, spend a bounded number of queries on it as a labelling instrument and distil. A companion paper uses these frozen predictions to quantify how evaluation design choices compare with model choice.

SysBio FAIRplex is a Common Fund Venture Program that catalogs and indexes data from the Accelerating Medicines Partnership(R) (AMP(R)) Program through a federated model in which data hosts retain custody of their datasets. The central piece of this work is the SysBio Common Data Model (SysBio CDM). AMP is a precompetitive public-private partnership started in 2014 that unites the resources of NIH and private partners to improve our understanding of disease pathways and transform current models for developing new treatments by: - identifying new targets, biomarkers, and development paradigms; - developing leading-edge tools and technologies; - collecting large-scale datasets and supporting analytics for open analysis by the public; and - generating consensus platforms and procedures. A multidisciplinary Task Force was chartered to design the SysBio CDM by extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model into the -omics domain. The Task Force produced a Minimum Viable Product comprising nine OMOP tables; four extension tables for assay and file metadata; and a Common Data Element (CDE) Registry to specify field semantics. This manuscript describes the deliverable: the underlying design choices, the criteria applied in selecting and constructing the extension tables, how the extended model supports multimodal data integration across AMP projects, and what further work to support additional -omics modalities would entail. As an auxiliary methodology, the paper also describes the AI-assisted CDE harmonization workflow used to populate the model.

Predicting disease risk from prior diagnoses is fundamental to clinical decision-making, particularly during health emergencies such as the COVID-19 pandemic, when individuals with long-term conditions may be disproportionately vulnerable to adverse outcomes. Despite intense interest in developing models to predict disease risk from prior diagnoses (1-3), most prediction models do not estimate effects of each prior diagnosis on disease risk conditional on other diagnoses, limiting interpretability and clinical utility. We developed the Comorbidity Risk Score (CRS), trained on 13 million individuals (age 40-69) from linked electronic health record (EHR) datasets of the entire population of England, to predict COVID-19 hospitalisation and 87 other disease outcomes. CRS was trained at close to saturated sample size and precisely estimated the effects of 212 prior diagnoses on the 88 disease outcomes, conditional on all other prior diagnoses. Correlations of CRS effect sizes across outcomes (e.g. 0.76 for myocardial infarction vs. hyperlipidaemia) matched the corresponding genetic correlations (e.g. 0.79 for myocardial infarction vs. hyperlipidaemia), confirming that comorbidity architectures capture disease aetiology. On average, CRS identified 5% of the population with 3.4-fold higher disease risk, including myocardial infarction (4.4-fold), lung cancer (6.5-fold), and COVID-19 hospitalisation (6.3-fold). Using prior diagnoses alone, CRS outperformed state-of-the-art clinical COVID-19 models (4). Furthermore, CRS (N=13 million) substantially outperformed state-of-the-art AI (1) (N=0.5 million) and linear (3) (N=0.5 million) models in predicting disease risk, suggesting that training sample size outweighs model complexity. CRS attained near-perfect transferability across self-reported ethnicities (e.g., Black vs. White: AUROC ratio = 97.3%). Finally, CRS distinguished independently predictive comorbidities from indirect associations, e.g., lipid metabolism disorder was a strong predictor of myocardial infarction risk but not ischaemic stroke, after conditioning on other prior diagnoses. In conclusion, CRS provides a comprehensive resource for understanding the impact of comorbidities on COVID-19 and other future diseases, revealing disease aetiology while enabling powerful prediction of disease risk.

Background: Polymorphisms in the N-acetyltransferase 2 (NAT2) gene explain much of the interindividual variation in isoniazid (INH) metabolism and determine risk of toxicities. However, there is limited evidence to guide INH dose adjustment according to the NAT2 acetylator profile in weekly rifapentine-INH tuberculosis preventive therapy (TPT). Methods: In a prospective, multicenter, within-subject PK trial (NCT05413551), adults initiating 3HP in Brazil were assigned genotype-guided INH doses (slow: 5 mg/kg <=300 mg; intermediate: 15 mg/kg <=900 mg; rapid: 25 mg/kg <=1,500 mg) alongside a standard 900 mg flat dose on an alternate occasion. AUC0-24 and C24 were estimated from serial blood samples; a two-compartment Michaelis-Menten population PK model characterized NAT2 effects on clearance. Results: Among 228 participants, 47.4% (108/228) were intermediate, 43.4% (99/228) slow, and 9.2% (21/228) rapid acetylators. Genotype-guided dosing reduced AUC0-24 variability approximately two-fold versus standard dosing (CV 58.8% vs 76.8%) and increased exposure uniformity (median AUC0-24 27.2 [IQR 18.8-41.3] vs 43.2 [27.3-71.0] mg h/L). Among slow acetylators, C24 >0.15 ug/mL decreased from 27/42 (64%) with standard dosing to 1/42 (2%) with genotype-guided dosing (P<0.0001). In 104 participants with intensive PK sampling, rapid acetylators receiving guided doses had AUC0-24 similar to standard-dose intermediate acetylators (42.8 vs 39.5 mg h/L; P=.63). Monte Carlo simulations supported doses of 600, 900, and 1,200 mg for slow, intermediate, and rapid acetylators, respectively. Conclusions: NAT2-guided isoniazid dosing reduced variation in drug levels, averting very low and high AUC and C24. These findings inform genotype-stratified dosing of INH for TPT, which might reduce toxicities and improve outcomes.

Background: The condition of a patient with acute stroke often changes within hours of ICU admission. Prognostic work here targets fixed endpoints predicted from admission data, and trajectory phenotyping assigns one label per patient. We used longitudinal ICU data to identify interpretable dynamic clinical states, characterize transitions between them, and relate the current state to later events. Methods: Retrospective cohort study of 6368 adults with acute stroke in MIMIC IV v3.1. The first 72 h were divided into twelve 6-hour windows, and a hidden Markov model was fitted to 21 neurological, physiological and organ support variables. State number was chosen against criteria fixed before fitting: statistical fit, restart stability, state occupancy and clinical interpretability. Generalized estimating equations related the current state to new mechanical ventilation and vasopressor use within 12 h, and to ICU death within 72 h. Eleven sensitivity analyses assessed the robustness of the state solution. Results: Four states were selected: neurologically preserved-low support, neurological impairment low support, impairment renal dysfunction and impairment-respiratory support (63.3%, 7.8%, 11.8% and 17.1% of windows). Within 72 h, 40.3% of patients changed state at least once, and transitions ran in both directions rather than along a single severity gradient. States were identified without outcome data, yet ICU mortality by last state ranged from 2.9% to 43.9%. Adjusted for age, sex, subtype and Charlson index, the current state remained associated with organ-support escalation and death. State prevalence differed by at most 1.1 percentage points between training and test sets, and 10 of 11 sensitivity analyses gave a stable four-state solution (ARI 0.754 0.955). Conclusions: The early ICU course of acute stroke can be represented as movement among a small number of clinically interpretable states. The representation was reproducible in a held out set and across admission eras, but requires validation in an independent database before any clinical use.

Background: In Denmark, perioperative high-dose glucocorticoid treatment were step-wisely implemented for total hip arthroplasty (THA), total knee arthroplasty (TKA), and unicompartmental knee arthroplasty (UKA). We aimed to estimate the effect of a single high dose of glucocorticoids on opioid consumption following primary THA, TKA, and UKA. Methods: This was a prespecified analysis of a multicenter natural experiment using electronic health record data. We included elective THA, TKA, or UKA surgeries performed in Eastern Denmark from 2017-2025. At each center, surgeries before implementation of high-dose glucocorticoids served as controls, whereas surgeries after implementation comprised the intervention group. The primary outcome was the between-group difference in cumulative 0-24h opioid consumption, which included preemptive end-of-surgery doses. The predefined minimal important difference was set at 5 mg IV morphine equivalents. Secondary outcomes were maximum 0-10 numerical rating scale (NRS) pain score and incidence of opioid-related adverse events within 24 hours, hospital length of stay, and days alive and out of hospital at 30 days. Results: A total of 47,317 surgeries performed at nine centers were analyzed: 13,010 controls and 34,307 in the intervention group. During the study period, five centers implemented high-dose glucocorticoids for THA patients, two for TKA/UKA patients. High-dose glucocorticoids were administered to 6% of patients before implementation versus 92% after. High-dose glucocorticoids resulted in a mean reduction of 3.8 mg intravenous (IV) morphine equivalents (95% CI 3.3;4.3). The intervention also reduced the maximum 0-24h NRS pain score by 0.8 points (99% CI 0.7;0.9), but there was no difference in adverse events, length of stay, or days alive and out of hospital. Conclusions: Implementation of high-dose glucocorticoids reduced 0-24-hour opioid consumption by 3.8 mg IV morphine equivalents after elective hip and knee arthroplasty. This difference was below the prespecified minimal important difference threshold. Online registration: https://doi.org/10.1101/2025.11.11.25339982