BMJ Sexual & Reproductive Health

Background: Unintended pregnancies can introduce stress and shift life trajectories. Social support may buffer these effects, yet its influence during an unintended pregnancy and into the early parenthood period is not clear. This study aimed to understand the types and gaps of social support experienced throughout this period. Methods: This study utilized interview data under the RISE UP study in The Hague, the Netherlands. 13 mothers and 8 partners who experienced an unintended pregnancy participated in semi structured interviews between 2024 and 2025. Interviews were thematically analyzed using House's social support framework. Results: Different types of support were highlighted across the entire timeline from pregnancy to early parenthood, underlining its dynamic nature. Emotional and instrumental support stood out the most throughout. A key form of emotional support was knowing that support is available, even if not needed immediately. Conclusions: Perceived support during unintended pregnancy is shaped more by contextual factors than by pregnancy intention. While emotional and instrumental support are valued throughout, their form differs by the family's unique circumstances, emphasizing the need for tailored support across the perinatal and postpartum periods.

Background: Postpartum recovery is a public health concern. The Obstetric Quality of Recovery-10 (ObsQoR-10) is a brief patient-reported outcome measure designed to assess early recovery after childbirth. Its validation is currently limited to the first three days postpartum. This study aimed to evaluate the psychometric properties of the ObsQoR-10 across the first 30 days postpartum. Methods:We conducted a cross-sectional psychometric evaluation of the ObsQoR-10 using baseline data from a national Swiss multilingual cohort (French, German, Italian, and English). Women were recruited within the first week postpartum and completed the ObsQoR-10 and the EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) at a single time point within 30 days postpartum. Clinical data were extracted from medical records. Analyses were performed across three postpartum windows (0-2, 3-7, and 8-30 days). Structural validity, measurement invariance, reliability, and construct validity (convergent and known-groups) were assessed. Results:A total of 1935 women were included. Structural validity supported a stable four-factor structure with excellent model fit (CFI 0.995-0.997; RMSEA 0.055-0.059), and bifactor analysis supported essential unidimensionality. Measurement invariance was confirmed at metric and scalar levels across postpartum windows. Reliability was good (Cronbach's alpha 0.83-0.86). Convergent validity was supported by moderate correlations with the EQ-5D-5L (;0.51 to 0.30), decreasing over time. Known-groups validity was demonstrated by significantly lower scores in women with poorer health status, postpartum haemorrhage, and operative or caesarean birth (all p <0.001). Conclusions:The ObsQoR-10 demonstrates consistent, valid, and reliable psychometric properties for assessing postpartum recovery across the first 30 days.

Abstract Objective To evaluate the adherence to guidelines and readability of large language model-generated sexual health information related to female sexual dysfunction following cystectomy, and to determine whether adherence differs across models and prompt formats. A secondary objective was to introduce an analytic strategy using principal component analysis to examine the dimensions of readability metrics. Methods Three large language models (LLMs), ChatGPT, Gemini, and Perplexity were prompted with six clinical questions related to sexual function after cystectomy. Questions were phrased in long-form and short-form language. Responses were independently graded by two reviewers, derived from guideline recommendations. Linear mixed-effects models predicted adherence as functions of LLM, prompt, and reviewer, with clinical questions as a random intercept. Readability was assessed using five metrics, and principal component analysis (PCA) was used to determine latent structure. Results ChatGPT demonstrated the highest (estimated marginal mean [emm] = 0.769), outperforming Gemini (0.499) and Perplexity (0.457). Shorter, less complex prompts elicited higher adherence than more complex, clinical prompts. All models produced content that exceeded recommended reading levels. PCA demonstrated that a single dominant component accounted for 76.7% of variance across readability indices, indicating a shared underlying construct. Conclusion ChatGPT produced the most guideline-concordant information overall. High linguistic complexity was seen across models, highlighting a barrier to patient comprehension. These findings characterize large language models as variable medical information systems whose outputs rely heavily on prompt structure and model type.

Purpose: Human papillomavirus (HPV) infection and HPV-associated diseases impact health-related quality of life (HRQOL). We aimed to measure HRQOL in women with HPV-positive test results, cervical intraepithelial neoplasia (CIN), or cervical cancer in Latin America. Methods: We enrolled women aged 18 to 75 years from Bolivia, Colombia, Honduras, Peru, and Uruguay. We used the EQ-5D-5L instrument to calculate EQ-5D index scores with country-specific sets of health preferences when available. We calculated medians and interquartile ranges (IQR) of EQ-5D index and conducted an exploratory analysis comparing HRQOL loss by diagnosis and country with a gamma regression. Results: We present results from 1,073 participants. Median age was 43 years (IQR: 34-52). The EQ-5D index scores by diagnosis were as follows: HPV-positive test alone 0.906 (standard deviation [SD]: 0.129), CIN1: 0.891 (SD: 0.136), CIN2: 0.892 (SE: 0.124), CIN3: 0.870 (SD: 0.138), and cervical cancer: 0.737 (SD: 0.268). HRQOL was associated with age, diagnosis, and country; there was a significant decreasing trend in HRQOL with worsening health state. Women with cervical cancer had a 3.19-fold higher HRQOL loss than women with an HPV-positive test alone or CIN1. We also found significant differences in HRQOL loss across countries, after adjustment for diagnosis and sociodemographic factors. Conclusion: HRQOL decreased with diagnosis severity, with significant differences between countries. To conduct cost-effectiveness modeling on HPV preventive interventions, obtaining accurate HRQOL estimates is essential. This process should consider the diverse local health preferences influenced by sociodemographic and cultural factors, as well as the population's beliefs and experiences regarding health.

Domestic gender-based violence (DGBV) remains a major public health concern that undermines womens sexual and reproductive health (SRH). This study assessed the influence of DGBV on SRH-seeking behavior among women of reproductive age in the Lusaaze Zone, Kampala District, Uganda. A quantitative cross-sectional descriptive-correlational design was employed among 383 women aged 15 to 49 years selected through systematic random sampling. The data were analyzed using descriptive statistics, chi-square tests and modified Poisson regression. The prevalence of DGBV was high, with 61.6% of women reporting public humiliation, 60.0% reporting physical violence, 59.0% reporting forced sexual intercourse, and 45.1% reporting economic exclusion. Women exposed to DGBV were nearly twice as likely to report partner prevention of HIV service access (66.8% vs. 35.2%; p < 0.001). Household financial control emerged as the strongest predictor of DGBV exposure, with women whose partner-controlled household income was approximately 2.4 times more likely to experience violence. The primary indicator for SRH-seeking behavior was STI treatment-seeking status, which was not independently associated with DGBV after adjustment (aPR=0.99, 95% CI=0.65 to 1.49). The study concludes that DGBV is highly prevalent and driven largely by unequal household power relations. Strengthening community DGBV prevention programs, womens economic empowerment, and the integration of DGBV response services within SRH programs are recommended.

Background Gestational diabetes mellitus (GDM) affects 1-in-7 pregnancies globally and is associated with significant short- and long-term health consequences. Although health behaviour change interventions can effectively reduce these risks, a significant implementation gap exists in translating this evidence into routine practice. Bump2Baby and Me (B2B&Me) was a mobile health (mHealth) coaching intervention provided to women at-risk of GDM from early pregnancy through to 1-year postpartum. B2B&Me Plus (B2B&Me+) aims to refine, implement and evaluate the implementation of this personalised intervention across 4 countries (Ireland, Spain, Poland and Norway) with differing health systems and population profiles. Methods This study employs a hybrid type 3 implementation-effectiveness design using a non-randomised ABA block approach within a longitudinal cohort. Participants will be screened using the Monash machine learning GDM screening tool (MMLGDST). During the intervention (Block B), women at risk of developing GDM will be offered access to a smartphone-based coaching application featuring 1:1 synchronous sessions, asynchronous text and video messaging along with a Bluetooth-enabled weighing scale for self-monitoring. Support continues from early pregnancy through to nine months postpartum. The studys primary objective is to evaluate reach, adoption, implementation and maintenance of the B2B&Me+ intervention programme when delivered within routine maternity care. Implementation success will be assessed using the RE-AIM framework, while secondary outcomes will assess intervention effectiveness. The study will examine population-level uptake at each site, evaluate the benefits and costs of implementation across varying contexts, and analyse how four different referral methods, randomised at the site level, affect uptake. In addition, a European implementation toolkit will be developed to provide health services with scalable strategies to bridge the evidence-to-practice gap. Discussion This study will contribute to a growing literature on the implementation of a successfully trialled mHealth intervention in a real-world context. Understanding variation in both intervention and implementation success within a routine maternity care context across diverse settings will inform the development of an implementation toolkit to support health services in reducing the incidence of GDM and improving maternal and child health outcomes.

Transformer-based deep learning models have shown great potential for decoding visual EEG signals. However, their internal attention mechanisms are often evaluated primarily on optimization objectives, leaving their alignment with biological brain connectivity an open question. This study empirically evaluates how variations in EEG preprocessing strategies affect these attention representations using the Adaptive Thinking Mapper (ATM) model as a framework. We compared a baseline pipeline (MVNN only) against a comprehensive cleaning pipeline integrating ICA and notch filtering. The models were evaluated through cross-generalization, noise robustness, and spectral-temporal ablation analyses. Furthermore, we investigated the structural correspondence between the model's data-driven attention weights and neurophysiological reference networks (GPDC, PDC, and DTF) using Node Strength Correlation and Representational Similarity Analysis (RSA). The results show that the comprehensive preprocessing successfully suppresses non-neural artifacts, such as frontal noise and electrical interference, while maintaining comparable decoding accuracy and baseline robustness. Alignment analyses revealed that the broad spatial organization of the learned attention patterns remains highly stable across pipelines, capturing key directed connectivity dynamics with subtle, metric-dependent variations in global representational geometry. This work provides an empirical exploration into bridging data-driven attention weights with neurophysiological consistency, offering insights toward more transparent brain-computer interfaces.

Brain areas differ in their inherent susceptibility to focal seizures, but the principles governing this risk remain unclear. While prior work has focused on anatomical and physiological factors, here we observed a fundamental contribution from the computations performed by the underlying neural network. Handcrafted and trained recurrent neural networks supporting continuous representations respond to seizure perturbations with higher activity and earlier performance decline relative to matched networks stabilizing discrete, well-separated states. Consistent with this prediction, in vivo recordings revealed that medial entorhinal cortex, whose grid cells exhibit continuous attractor dynamics, drives acute epileptiform discharges with stronger involvement and smoother state trajectories compared to CA3, a hippocampal subfield associated with discrete memory storage. Moreover, selective synaptic silencing demonstrated that this difference in seizure responses depends on intact entorhinal connectivity. Thus, the computations that enable neural networks to process information also influence their vulnerability to pathological transitions.

Recent advances in spatially resolved transcriptomics (SRT) enabled measurement of sets of pathway genes activity within tissues. However, existing gene set activity scoring methods overlook spatial dependencies among tissue locations, restricting their ability to capture region-specific pathway activities associated with disease pathology or cellular communication. Moreover, these methods lack significance-level inference for activity scores, provide limited interpretability of gene-level contribution to a pathway, and scale poorly to advanced large-size SRT datasets. To address these limitations, we present GESSO (Gene sEt activity Score analysis with Spatial lOcation), a spatially informed gene set scoring method adaptable to diverse SRT platforms. GESSO models gene set activity levels through a graph-regularized matrix decomposition algorithm, jointly inferring spatially coherent gene set activity scores (GASs) and interpretable metagene weights that capture gene-level contributions. It further implements a permutation-based local significance test and a stratified low-resolution approximation that scales to high-resolution SRT datasets such as Visium HD, Stereo-seq, and Xenium Prime. Across 13 datasets from five SRT platforms, GESSO outperformed all existing methods in accuracy, calibration, interpretability, and scalability. Applications revealed novel biological programs, including spatially confined EMT activation within tumor-stroma interfaces, developmental signaling gradients across embryonic tissues, and coordinated B-cell, T-cell, and signaling pathways within germinal centers of human lymph node tissue, revealing the spatial organization of immune function at subregional resolution.

The spatial arrangement of immune cells in the tumor microenvironment (TME) varies widely, from dispersed to clustered and tumor excluded to infiltrating. Multiplexed spatial profiling is an effective means of characterizing tumor-infiltrating lymphocytes (TILs) and immune complexes such as tertiary lymphoid structures (TLS) in the TME. However, few approaches have been described for objectively parametrizing patterns of immune organization and assessing their association with biological or clinical variables. This makes it difficult to evaluate whether a set of tumors is relatively immunologically cold or hot. Here we describe an intuitive set of statistical tools (available in the R package, tlsR) for characterizing lymphocyte patterns in the TME of solid cancers. We apply tlsR to primary prostate cancer (PCa), which is often described as immunologically cold. Using a cohort of 29 radical prostatectomy specimens stratified into low Gleason-grade (LGG; n=15) and high Gleason-grades (HGG; n =14) we show that HGG PCa is significantly more infiltrated than LGG PCa with lymphocytes organized into B cell or T cell enriched immune clusters (BICs and TICs). A subset of these ICs have the B and T cell zonation and follicular dendritic cells characteristic of a bona fide TLS. HGGs are also enriched with ICs containing precursor exhausted T cells (Tpex) and proliferating B cells and their tumor compartments harbor granzyme-B+ cytotoxic T cells in contact with cancer cells. Thus, far from being cold, a subset of HGG PCa has features associated with active immune surveillance, a finding with implications for emerging PCa immunotherapies.

Whether different pathogens acquire resistance to antimicrobials through the same mutations is a major question in evolution and microbiology. Most antifungal drugs are used to treat infections caused by multiple fungal species, many of which have diverged for millions of years. If the evolution of resistance was to converge onto the same set of mutations across species, knowing the mechanism of resistance in one would allow us to predict and track it in others. The extent of this convergence remains unknown due to the lack of systematic data on resistance mutations. Here, we quantify the conservation of resistance mutations in the cytosine deaminase (CD), a protein responsible for resistance to flucytosine, one of the oldest antifungal drugs. By comparing the crystal structures of this enzyme through 600 My of evolution, we show that the CD structure is highly conserved. We compared the full CD mutational resistance spectrum of resistance from an ascomycete and a basidiomycete. We found that resistance mutations in one ortholog can be used to predict resistance in the other at a high level of accuracy. However, because of epistasis, around 10% of mutations have distinct effects in the two orthologs, which imposes an upper limit to the transferability of the knowledge of resistance mutations from one species to another. Using biochemical assays and by structural characterization of several mutants, we identify distinct mechanisms of epistasis, one important being that the local physiochemical environment of some position has evolved in a way that makes the same substitutions destabilizing or entirely inactivating in an ortholog-specific manner. Our results show that resistance mutations can be conserved in fungi across hundreds of millions of years of evolution but that epistasis eventually limits the accuracy of these predictions.

The incidence of early-onset colorectal cancer (CRC) has risen sharply in recent decades1, yet the biological basis underlying the distinct behavior of tumors arising in young versus aged tissues remains poorly understood. Here we show that aging reprograms the epigenetic landscape of the colon, restricting colon tumor growth through stable silencing of developmental and fetal gene programs. We find that colon tumors arising in aged mice are intrinsically less proliferative than those arising in young animals. Multi-omic profiling of normal colon and colon tumors reveals that aging drives DNA hypermethylation, loss of Polycomb-associated chromatin states, and reduced chromatin accessibility at a defined set of developmental genes that are bivalent (marked by both H3K27me3 and H3K4 methylation), transcriptionally active in colon tumors from young animals and repressed in both tumors and normal tissue from old animals. Among the genes most strongly repressed in old animals is Tacstd2 (Trop2), a regulator of fetal intestinal programs and epithelial stemness. Pharmacologic inhibition of DNA methylation reactivates the aging-silenced gene network in organoids from old animals, whereas genetic disruption of Tacstd2 suppresses growth and developmental transcriptional programs in young tumor organoids. TACSTD2, fetal gene signatures, and the aging-associated bivalent gene program are likewise repressed in late-onset vs. early-onset human colorectal cancers. Collectively, these findings identify age-associated epigenetic silencing of developmental gene programs as a causal mechanism that constrains colorectal tumor growth and provide a mechanistic framework for understanding the distinct biology of early-onset colorectal cancer.

Extrahepatic delivery of lipid nanoparticles (LNPs) to non-phagocytic cells is a major challenge, with the leading strategy involving surface functionalization with target-specific monoclonal antibody (mAb) ligands. We investigate the stability of mAb-conjugated LNPs using two anchoring systems: the commonly used DSPE-PEG2kDa-maleimide and a block copolymer, PCL5kDa-b-PEG2kDa -maleimide, with the hypothesis that conjugation to a 150,000 Da antibody could overwhelm the relatively small ~600 Da aliphatic anchor on the PEG-lipid in vivo. Shedding of the mAB would compromise targeting. Conjugation integrity following IV injection was assessed by tagging LNPs and mAbs with metal ion tracers that could be quantified by ICP-MS. Results show that DSPE-PEG-mAb rapidly (within 1h) dissociates from LNPs in blood, leading to accelerated LNP clearance. In contrast, mAbs conjugated using PCL-b-PEG remained stably associated with the LNP over the 24h circulation and clearance of the construct. Results are connected to a thermodynamic model that reproduces experimental findings for PEG-anchor(-mAb) shedding in vitro and in vivo. This study identifies anchoring strength as a critical, unconsidered parameter for in vivo performance when conjugating mAbs to LNPs for extrahepatic delivery.

Blockade by sedimentary particles, such as mineral crystals, is a continuous risk the kidney tubule faces. To prevent that, kidney resident macrophages form transepithelial protrusions and remove intratubular sedimentary particles, a behavior particularly prevailing in the medulla over the cortex. However, the molecular mechanisms underlying this characteristic behavior of medulla macrophages are incompletely understood. In this study, we identified that the medulla had higher mechanical stiffness than the cortex in steady state, which was further elevated when kidney stone formed. Increased tissue rigidity was sensed by medulla macrophages via mechanoreceptor Piezo1, which promoted macrophage protrusion formation and their ability to clean the tubules. Loss of Piezo1 expression in kidney macrophages predisposed mice to intratubular accumulation of mineral crystal in steady state and accelerated kidney stone formation during oxalate intake challenge. Signaling via Piezo1 mobilized molecules involved in cell adhesion and protrusion assembly, including Talin2 and focal adhesion kinase (FAK). Finally, we developed a first-of-its-kind cell-based therapy for the treatment of experimental nephrolithiasis by exploiting macrophage Piezo1 activity, and this strategy shows great promise for future translational research.

Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimer's disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear. We present the SEA-AD DREAM Challenge, an open, international, model-to-data competition built on the Seattle Alzheimer's Disease Brain Cell Atlas to predict Alzheimer's disease neuropathological severity from single-nucleus RNA-sequencing data. Participants developed containerized models to predict categorical neuropathological staging, including overall Alzheimer's disease neuropathologic change, Braak stage, Thal phase, and CERAD score, as well as quantitative amyloid-{beta} and phospho-tau burden measured by 6E10 and AT8 immunohistochemistry. Across 17 eligible teams from 15 countries, the crowdsourcing framework enabled systematic comparison of diverse computational approaches and surfaced a broad landscape of modeling strategies and candidate predictive features. Top-performing methods achieved near-perfect prediction of categorical staging, with the best submission reaching a quadratic weighted kappa of 1.0 for the Overall AD Neuropathological Change score (ADNC), and competitive prediction of quantitative pathological burden in held-out data, with a best concordance correlation coefficient of 0.48. Post hoc perturbation analyses revealed that top categorical-stage predictions relied heavily on donor-level metadata-driven signals rather than transcriptomic features, whereas quantitative pathology prediction was more robust and supported by transcriptomic and cell-type-associated features with potential biological relevance to AD progression. The challenge also introduced the first AI Agent Track in a DREAM Challenge, providing an early benchmark for autonomous and human-guided agentic model development in single-cell neuroscience. This work demonstrates that single-nucleus transcriptomes encode substantial information about Alzheimer's disease pathology, establishes a reproducible benchmark for molecular neuropathology prediction, and highlights critical principles for designing privacy-preserving, leakage-aware community challenges using deeply phenotyped human brain data.

Temporal niche partitioning is a strategy for reducing interspecies competition and strengthening reproductive isolation. It relies on animals confining their daily activity to distinct diurnal, crepuscular, or nocturnal windows. However, a hardwired temporal niche is only advantageous under stable, predictable ecological regimes; surviving dynamic environments demands behavioral flexibility. Yet, it remains unclear how animals override rigid biological constraints to rapidly exploit transiently available fitness-critical time windows. To address this, we leveraged the twilight-active, species-rich Drosophila genus and monitored their daily activity under naturalistic conditions. Here, we show that intense sociosexual interactions rapidly drive a species-specific reformatting of their canonical crepuscular niche. The dominant sensory modality used for sexual communication predicts niche shift direction: reliance on chemosensation for courtship redirects behavioral activity into the night, while visual reliance shifts it into the day. This temporal plasticity bypasses the circadian clock, instead operating via a conserved dopaminergic pathway. Dopamine operates a dual-output brain circuit that simultaneously inhibits sleep and sustains sexual motivation. Our results reveal how mating imperatives decouple behavioral timing from circadian command, enabling conditional colonization of otherwise restricted temporal windows. Ultimately, by driving the divergence of previously overlapping niches, sociosexually induced temporal plasticity provides a powerful mechanism for sympatric coexistence in crowded environments.

Representations of the same physical stimulus vary between individuals. Characterizing individual differences has practical implications, but is challenging because these representations are not directly observable. Given a model of how representations vary within a population, we propose a Bayesian adaptive procedure for estimating an individual observer's representation from a series of targeted perceptual discrimination judgments. A key component of our approach is using Fisher information to identify stimulus distortions that efficiently differentiate observers in the population. As a proof of concept, we focus on individual differences in color perception and simulate observers with cone fundamentals drawn from an individual colorimetric observer model. We demonstrate that our approach can recover key aspects of a sampled observer's cone fundamentals using simulated three-alternative forced-choice oddity judgments with approximately 500 trials, corresponding to an experimental duration of approximately one hour. Our Bayesian adaptive framework provides a promising and generalizable approach to efficiently link behavioral measurements to individual differences in sensory representations.

We showed before that the endogenous peptide Nociceptin blocks the premature differentiation of oligodendrocytes (OLGs), preventing untimely precocious myelination in the developing brain. Consistent with this early function, Nociceptin brain expression is developmentally regulated, sharply decreasing with the initiation and progression of myelination. However, we now found that at difference with controls and relapsing-remitting multiple sclerosis (RRMS), Nociceptin levels are highly elevated in cerebrospinal fluid from patients with the most severe progressive MS (PMS) forms. This questioned whether Nociceptin early developmental effects could be latter recapitulated, interfering with remyelination in PMS. This possibility was tested by inducing experimental autoimmune encephalomyelitis in older mice, at an age equivalent to that with increased risk of RRMS transition into PMS. Older animals develop persistently highly debilitating clinical symptoms, and display both brain and spinal cord demyelination. Importantly, these mice exhibit elevated brain Nociceptin levels, and their treatment with an antagonist of the Nociceptin receptor (NOR) elicits a regression of clinical scoring that is accompanied by higher ratios of OLGs/OLG progenitor cells, increased myelination, and reduction of reactive astrocytes. These findings suggest that Nociceptin may be a crucial player in the age-related progression of MS; interfering with OLG maturation and remyelination, and perhaps further exacerbating neurological dysfunction by targeting astrocyte populations. The upregulation of Nociceptin secretion by human astrocytes in response to proinflammatory cytokines, also points to this peptide as a mediator of microglia-astrocyte interactions supporting MS progression with aging. NOR may offer a novel pharmacological target for ameliorating the devastating effects of MS progression.

Background: Compulsive alcohol use despite negative outcomes is a defining characteristic of alcohol use disorder. Rats exposed to long-term intermittent alcohol access (IAA) demonstrate sustained motivation for ethanol despite presence of the bitter additive quinine, offering a useful preclinical model of compulsive alcohol use. However, little is known about the role of habenular circuitry in the development of this phenotype. Here, we employed chemogenetic techniques targeting basal forebrain (BF) input to the lateral habenula (LHb) to probe the involvement of this neural circuitry in aversion-resistant alcohol consumption. Methods: Following long-term IAA or control conditions, male and female Long-Evans rats underwent surgery for the expression of designer receptors in BF-to-LHb projections. We then excited this pathway in rats with IAA history, or inhibited this pathway in rats with more limited ethanol history, before testing consumption of unadulterated and quinine-adulterated ethanol as well as unadulterated and quinine-adulterated sucrose. Results: Long-term IAA elevated ethanol drinking in all rats and aversion-resistant ethanol preference in males. Chemogenetic activation of BF-to-LHb neurons in rats with IAA history produced different effects in males and females: excitation enhanced ethanol intake in females, but reduced ethanol preference in males, regardless of quinine adulteration. Activation also led to a relative insensitivity to quinine-adulteration of sucrose when compared to controls, particularly in females. Chemogenetic inhibition in rats with limited prior ethanol exposure did not alter either ethanol or sucrose consumption with or without quinine. Conclusions: Our results suggest a differential role for BF-to-LHb circuitry in ethanol drinking based on sex, and a potential role for this circuitry in the sensitivity to quinine in the context of natural reward consumption.

Joint music making offers an ecologically powerful framework for investigating human social interaction and synchronization. Yet, experimental paradigms often rely on traditional instruments that limit accessibility, reproducibility, and experimental control. In parallel, the use of music for therapy and rehabilitation is expanding, motivating the development of digital musical instruments that can serve research, educational, and clinical purposes. Here, we introduce the e-Music Box Roma (eMB Roma), an open, reproducible digital musical instrument designed to study music making behavior regardless of musical training. The eMB Roma plays preregistered music with tempo controlled by hand rotary movements. Building on the original e-Music Box (Novembre et al., 2015), the eMB Roma retains its intuitive rotary hand control while introducing major innovations: a fully open and 3D-printable design, modular hardware with integrated slider and button controls, polyphonic output with multiple simultaneous instruments, and MIDI compatibility. Additionally, a dedicated graphical user interface allows real-time monitoring, experiment control, device synchronization (like neuroimaging or motion capture devices), and both solo and joint music-making paradigms. The eMB Roma provides a flexible and accessible platform for research contexts, allowing experimental control, reproducibility, and future extensions. Its open design and modularity make it suitable not only for research but also for therapeutic, rehabilitation, and educational applications, where it can support personalized interventions and quantitative assessment of motor performance.