Scientific Reports

Zebrafish have been used a prominent model for high-throughput phenotypic screens of candidate risk gene mutations for several disorders. This also includes models for attention deficit/hyperactivity disorder (ADHD). Traditional behavioural tests, such as the forced light/dark assay, concentrate on basic locomotion measures. However, recently developed visually-driven locomotion assays, for example closed-loop systems using virtual reality, have allowed extraction of richer data on animal locomotion and decision-making under different sensory stimuli. Here, we have used such a system to assess the behaviour in adgrl3.1 mutant fish, an established model for ADHD. Our results show that mutants exhibit a higher baseline excitability and a lower threshold for initiating motor events, demonstrating that collecting behavioural responses in an interactive environment enables a more precise characterisation of ADHD-relevant phenotypes associated with adgrl3.1 disruption. More generally, we establish a scalable translational platform to screen gene-function relationships and possible therapeutic interventions, not only for ADHD but multiple neurodevelopmental disorders.

Radiation-induced intestinal injury is a widely used model for studying mechanisms regulating tissue injury and regeneration. Traditionally, Cesium (137Cs) radiation has been used in research applications, but over the past decade, X-ray irradiation has become increasingly favored due to its improved safety and non-radioactive profile. Since each type of radiation has distinct physical characteristics that drive its performance, we sought to systematically compare the effects of the X-ray and 137Cs irradiators on intestinal epithelial injury and regeneration. Using established in vitro models, including colorectal cancer cell lines such as HCT116, RKO, and DLD-1, and mouse intestinal organoids, alongside an in vivo model, Bmi1-CreER;Rosa26eYFP, we evaluated differences in transcriptional, protein, and histopathological responses to irradiation. Our results demonstrate that X-ray produced intestinal injury and regenerative responses comparable to those induced by 137Cs, supporting its reliability as an alternative modality for studying intestinal radiation.

We completed a video-based, four-night, in-home, level 3 sleep apnea study of healthy, low-risk pregnant participants and their bed partners in order to characterize sleep physiology in the third trimester of pregnancy. Demographic, anthropometric, and baseline sleep health characteristics were recorded, and the NightOwl home sleep apnea test device was used to measure sleep breathing, posture, and architecture parameters. Symptoms of restless legs syndrome were elicited in the exit interview. Forty-one pregnant participants and 36 bed partners completed the study. Bed partners had a significantly higher prevalence of sleep apnea than their pregnant co-sleepers (31% vs. 5.9%). Bed partners also had more severe sleep apnea than their pregnant co-sleepers, and this persisted on an adjusted analysis for baseline differences in factors known to increase risk of sleep apnea. In pregnant participants, increasing gestational age was found to be protective against mild respiratory events but not more severe events. While the correlation between STOP-Bang score and measures of sleep apnea severity was weak, an affirmative response to the witnessed apneas item on the STOP-Bang questionnaire was a strong predictor of more severe sleep apnea for all participants. Smoking history also increased sleep apnea risk. Pregnant participants had lower sleep efficiency and longer self-reported sleep onset latency. Restless legs syndrome was experienced by 39.5% of the pregnant participants but no bed partners. From a sleep breathing perspective, people with healthy, low-risk pregnancies have better sleep than their bed partners despite lower sleep efficiency and higher rates of restless legs syndrome.

Objective: To demonstrate the proof of principle that machine learning (ML) can be used to quantify Gleason Pattern (GP) 4 on digitized biopsy slides using multiple measurement approaches, allowing direct comparison of their prognostic performance. Methods: We assembled a convenience sample of 726 patients with grade group 2-4 prostate cancer on systematic biopsy who underwent radical prostatectomy between 2014 and 2023. Digitized biopsy slides were analyzed using a machine-learning algorithm (PAIGE-AI) to quantify GP4 using multiple measurement approaches, particularly with respect to how gaps between cancer foci (interfocal stroma) were handled. GP4 extent was quantified using linear measurements or a pixel-based area metric. Discrimination of each GP4 quantification approach, along with Grade Group (GG), was assessed for adverse radical prostatectomy pathology and biochemical recurrence. Results: We identified 15 different quantification approaches and observed differences between their discrimination. The highest discrimination was in the pixel-counting method (AUC 0.648). GP4 quantification outperformed GG for predicting adverse pathology (AUC 0.627 vs 0.608). Amount of GP3 was non-predictive once GP4 was known. These findings were consistent for BCR. Conclusions: We were able to measure slides using 15 distinct measurement approaches and replicated prior findings using ML to quantify GP4. Our findings support the use of ML as a research tool to compare different GP4 quantification approaches. We intend to use our method on larger cohorts to determine with which measurement approach best predicts oncologic outcome.

Road traffic accidents represent a global public safety crisis, necessitating advanced computational tools for accurate injury severity prediction and effective decision support. This study evaluates high-performing ensemble machine learning models, including AdaBoost, XGBoost, LightGBM, HistGBRT, CatBoost, Gradient Boosting, NGBoost, and Random Forest, using a comprehensive National Highway Traffic Safety Administration (NHTSA) dataset from 2018 to 2022. While all models demonstrated exceptional predictive accuracy, with HistGBRT achieving the highest overall accuracy of 92.26%, a defining achievement of this work is the perfect classification (100% precision and recall) of fatal injuries across all ensemble architectures. To bridge the gap between predictive performance and actionable intelligence, this research integrates SHapley Additive exPlanations (SHAP) to provide both global insights into dataset-wide risk factors and local, instance-specific rationales for individual crash events. The global analysis identified ethnicity, airbag deployment, and harmful event type as primary drivers of injury severity, while local force and waterfall plots revealed the precise "push and pull" of variables for specific incidents. The results offer a robust, interpretable framework for stakeholders tasked with improving traffic safety and mitigating crash-related harm.

Interpreting machine learning models typically relies on feature attribution methods that quantify the contribution of individual variables to model predictions. However, it remains unclear whether attribution magnitude reflects the true functional importance of features for model performance. Here, we present a unified interpretability framework integrating permutation-based attribution, feature ablation, and stability under perturbation across multiple feature spaces. Using nested cross-validation and permutation-based null diagnostics, we systematically evaluate the relationship between attribution magnitude and functional dependence in clinical and biomarker-based prediction models. Attribution magnitude is frequently misaligned with functional importance, with weak to strong negative correlations observed across feature spaces (Spearman {rho} ranging from -0.374 to -0.917). Features with high attribution often have limited impact on model performance when removed, whereas features with low attribution can be essential for maintaining predictive accuracy. These discrepancies define distinct classes of interpretability failure, including attribution excess and latent dependence. Interpretability further depends on feature space composition, and stable, functionally relevant features are not necessarily those with the highest attribution scores. By integrating attribution, functional impact, and stability into a composite Feature Reliability Score, we identify features that remain informative across perturbations and analytical contexts. These findings indicate that interpretability does not arise from attribution magnitude alone but is better characterized from stability under perturbation. This framework provides a basis for more robust model interpretation and highlights limitations of attribution-centric approaches in high-dimensional and correlated data settings.

Standard analysis of amyotrophic lateral sclerosis (ALS) clinical trials evaluates therapeutic efficacy by comparing linear slopes of total ALS Functional Rating Scale (ALSFRS) scores between treatment arms. This approach compresses multidomain ordinal data into a single scalar trajectory, discarding distributional structure. When subgroup-level trends differ in timing or direction, such aggregation can attenuate or eliminate them, a phenomenon known as Simpsons paradox. Here we apply Shannon entropy, computed from item-level score distributions within each ALSFRS functional domain following the framework established in [8], to the PRO-ACT database, stratified by treatment arm (Active: n = 4,581; Placebo: n = 2,931; 19 monthly time points). The entropy trajectories of drug-treated and placebo populations diverge visibly and systematically across all four functional domains (Bulbar, Fine Motor, Gross Motor, Respiratory). In the Fine Motor domain, the placebo population reaches peak entropy at month 8 and reverses, while the active population does not peak until month 13, a five-month delay in the populations transit toward functional loss. This divergence is model-independent: it is present in the raw Shannon entropy trajectories before any dynamical model is applied. A permutation test shuffling patient-level arm labels (n = 1,000 permutations) confirms that the total integrated absolute divergence across all four domains exceeds the null distribution at p < 0.001 (observed: 4.48; null: 2.03 {+/-} 0.33; 7.5 standard deviations above the null mean), with Fine Motor (p = 0.001) and Respiratory (p < 0.001) individually significant. The quantity that differs between arms, the shape and timing of the populations distributional evolution, does not exist as a measurable quantity in the total-score linear-slope framework used to evaluate these trials. Whether this signal reflects genuine treatment effects, compositional artifacts from pooling heterogeneous trials, or both cannot be determined from the anonymized public database alone. What can be determined is that the standard ALS clinical trial endpoint makes an implicit assumption, that the distributional information it discards is uninformative, and the present results demonstrate empirically that this assumption is false.

Research in cognitive neuroscience has relied on simple, highly controlled stimuli due to the difficulty in developing standardized, ecologically valid stimulus sets. However, there is a consensus that using ecologically valid stimuli is imperative to generalize results beyond controlled laboratory settings. The current study introduces a naturalistic audio stimulus database, consisting of short, recognizable, and emotionally rated stimuli. To create such a database, the current study collected 291 audio files from a wide range of sources. 361 participants rated the audio clips on emotionality, arousal, and recognizability, and subsequently freely described the audios by typing what they believed the sound to be. The text responses of the participants were embedded and clustered using an unsupervised machine-learning algorithm to derive a participant-grounded organization of auditory object categories. The results indicate audio clips were easily recognizable, while emotionality and arousal ratings showed broad variability, making the database suitable for diverse experimental needs. Furthermore, the final database comprises 10 distinct semantic categories, providing a diverse set of auditory stimuli.

Routine ICU data offers valuable insights into daily physiological rhythms. While traditional methods assume these cycles maintain fixed periods and amplitudes, their inherent variability requires dynamic estimation of instantaneous trends. Wavelet transform effectively resolves circadian oscillations, especially for frequently measured vital parameters. We present novel extensions to the Continuous Wavelet Transform (CWT) power spectral analysis to better detect and segment subtle temporal patterns. Using this approach, we uncover hidden circadian patterns in cardiovascular vitals such as Heart Rate (HR) and Mean Blood Pressure (MBP) measured over five days in a retrospective cohort of 855 ICU patients. By quantifying non-stationary rhythms, we identified diurnal and semi-diurnal oscillations varying in period and power according to delirium and deep sedation. Notably, HR exhibits a clear diurnal and semi-diurnal rhythm when delirium is absent. Overall, our framework supports the CWT as a powerful tool for analyzing complex physiological signals, particularly vital signs. Crucially, our findings suggest that cardiovascular rhythm disruption can be associated with ICU-related delirium and deep sedation.

A distinctive feature of bodily experience is its transparency. During skilled action, our limbs recede from awareness and function as the medium of interaction rather than perceptual objects1. This is reflected in systematic perceptual biases: humans reliably underestimate the weight of their own hands2, potentially reflecting predictive motor processes that modulate self-generated sensory signals. Wearable technologies may test the limits of this perceptual transparency. Exoskeletons and other augmentative devices attach directly to the body, adding mass that must be integrated into sensorimotor control3; yet little is known about how such devices are experienced as they become integrated into the sensorimotor system. Here, we tested whether training with finger-extending exoskeletons alters their perceived weight and whether such changes depend on active use. We developed a Bayesian analytic framework combining individual psychometric modelling with a regression-based decomposition of perceived weight, to partition contributions of the biological hand and attached exoskeletal device. Thirty-four right-handed adults completed a weight-perception task before and after 20 minutes of training with either finger-extending or non-augmenting control devices. Participants compared the perceived weight of their right hand, with or without the exoskeleton, to reference weights suspended from the opposite wrist. Before training, the weight of both the biological hand and the exoskeleton were underestimated to a similar degree ([~]25- 30%), suggesting rapid perceptual integration following attachment. Training selectively increased attenuation of the perceived weight of the finger-extending exoskeleton, with no corresponding change for the biological hand and little evidence for a general training effect. These findings support a two-stage embodiment process in which passive attachment initiates perceptual updating, while sensorimotor training consolidates integration through functional interaction with the device. Perceived weight thus provides a behavioral marker of embodiment, offering insight into how the sensorimotor system integrates wearable augmentative technologies.

Emotions often occur within social interactions where affective cues are accessible or inferable by others. This raises questions regarding how and to which degree social context modulates subjective, physiological and behavioural affective responses, as well as their coherence, questions which remain points of tension in emotion research. To investigate this, we measured subjective affective ratings, autonomic sympathetic and parasympathetic activity, and facial behaviour while participants completed an emotion-induction task. In the social-context condition (but not control), participants believed that their video feed was accessible to a potential future interaction partner. Results show that even such "minimal social context" selectively and differentially modulated affective response modalities, characterised by both intensification of autonomic responses and dampening of overt facial and subjective affect. Multivariate dimensionality analysis further identified a cross-modal affective dimension Interestingly, social context reduced participants coupling with this shared affective response structure, indicating weaker cross-modal coherence. These findings suggest that emotional responding relies on a flexible, rather than rigid, configuration of affective features, likely recruited to meet the socioemotional demands of a given context. This has important implications for understanding the structure and function of emotion, as well as typical and atypical socioemotional responding.

BackgroundIn patients requiring respiratory support, clinicians rely on physical exam, radiologic, laboratory, and ventilator-derived measures for the provision of sufficient support while minimizing ventilator and "work of breathing" induced lung injury. Point of care lung ultrasound (LUS) is a widely available tool in hospital and clinic environments. To date, LUS has not been used to evaluate lung strain. MethodsWe collected LUS images in four anesthetized, neuromuscularly blocked, and mechanically ventilated pigs being used for another experiment. A feature tracking tool was developed which tracked echo-bright lung structures in ten second clips obtained in triplicate of the right and left, upper and lower lung fields using tidal volumes of 4, 6, 8, 10, and 12 mL/kg. Pleural lines were manually drawn and a program for quantifying lung strain developed with assistance from Anthropic Claude Artificial Intelligence tool. Structures were identified in inspiratory and expiratory frames and tracked bidirectionally with median strain per frame used for calculations. ResultsTriplicate measures of lung ultrasound images in four pigs had a median coefficients of variation of 35% (23-47% IQR) and linear modeling of strain with tidal volumes of 4-12 mL/kg showed positive correlation with R2 value ranging from 0.89 to 0.97. Strain measurements were similar after bronchial administration of 1.5M hydrochloric acid. ConclusionsRegional lung strain quantification using LUS is a viable and potentially useful tool for respiratory support management.

BackgroundManual video-based evaluation of surgical skills can be time-consuming and delays trainee feedback. Artificial intelligence (AI) offers opportunities to automate aspects of assessment while maintaining clinician oversight. We developed an interpretable spatiotemporal model that classifies surgical expertise directly from endoscopic video in standardized training tasks and generates saliency-based "highlights reels" showing the most influential frames. MethodsAn RGB pipeline combining InceptionV3 for spatial feature extraction and a gated recurrent unit (GRU) for temporal modeling was trained on the JIGSAWS dataset. The model outputs novice, intermediate, or expert labels. A rolling-window, low-latency evaluation at 30 fps with a stride of 10 frames was used. A motion-augmented variant fused RGB with optical-flow features. Spatial and temporal saliency maps highlighted key decision-making regions. ResultsThe RGB model achieved 95% accuracy (F1: 92% expert, 86% intermediate, 99% novice). Performance was strongest for novice and expert trials, while intermediate trials showed the lowest recall, consistent with greater ambiguity around the intermediate skill level. Saliency maps consistently emphasized tool-tissue interactions and peaked during technically demanding phases. The optical-flow variant underperformed, approximately 38% accuracy, which may reflect sensitivity to global camera motion and other non-informative motion patterns. ConclusionsThis interpretable AI pipeline accurately classifies surgical skill while producing intuitive visual highlights. Future work will refine highlight thresholds and validate on laparoscopic inguinal hernia repair for realworld deployment.

Aims The oral glucose tolerance test (OGTT) is effective for detecting post-load dysglycemia, but it is burdensome and therefore not routinely used. Continuous glucose monitoring (CGM) offers a convenient way to capture real-world glucose patterns, yet it remains unclear whether CGM-derived metrics reflect OGTT-defined dysglycemia. We therefore aimed to evaluate CGM-derived and clinical metrics for predicting OGTT 2-hour glucose, classifying OGTT-defined dysglycemia, and assessing day-to-day repeatability. Methods We analyzed a cohort with paired free-living CGM and OGTT. Multiple CGM-derived metrics and clinical measures were compared for prediction of OGTT 2-hour glucose, classification of OGTT-defined dysglycemia, and day-to-day stability. Predictive performance was assessed primarily by leave-one-out (LOO) R^2, and day-to-day repeatability by intraclass correlation coefficients (ICC). Results The glycemic persistence index (GPI), a metric integrating the magnitude and duration of glycemic elevation, was the strongest single predictor of OGTT 2-hour glucose (LOO R^2 = 0.439). GPI also showed strong day-to-day repeatability (ICC = 0.665) and ranked first on a combined prediction-stability score. For classification of OGTT-defined dysglycemia, HbA1c had a slightly higher AUC than GPI, but GPI plus HbA1c performed best overall, indicating complementary information. Conclusions GPI was a strong predictor of OGTT 2-hour glucose and showed a favorable balance between predictive performance and day-to-day stability, supporting its potential utility as a CGM-derived marker of dysglycemia.

Chronotype reflects individual circadian preference for timing of sleep, wakefulness, and peak performance and has been linked to variability in prefrontal cognitive function across the day. Whether chronotype independently relates to dual-task gait cost (DTC) and whether this relationship differs by cognitive task domain is unclear. Sixty-nine healthy young adults (37 female; mean age 21.3 years) completed the Morningness-Eveningness Questionnaire (MEQ). Spatiotemporal gait parameters were recorded with three-dimensional motion capture during single-task walking and three dual-task conditions: backward word spelling (5LWB; phonological), serial subtraction by seven (SS7; arithmetic), and reverse month recitation (RMR; sequential). DTC was calculated for eight gait parameters. Condition differences were assessed with nonparametric tests and post-hoc comparisons. Multiple linear regression, adjusting for age, sex, BMI, and baseline gait velocity, tested the independent association between MEQ score and mean velocity DTC; exploratory Spearman correlations examined other parameters. SS7 produced the largest mean velocity DTC (-12.76%), significantly greater than 5LWB (-7.95%; p = 0.002) and RMR (-9.57%; p = 0.021). MEQ score independently predicted mean velocity DTC in 5LWB ({beta} = -0.51, p < 0.001, R{superscript 2} = 0.269) and RMR ({beta} = -0.55, p = 0.004, R{superscript 2} = 0.222), indicating greater morningness associated with better gait-speed preservation under cognitive load; the SS7 association was not significant ({beta} = -0.33, p = 0.071). Exploratory correlations showed MEQ-DTC associations across 7/8 parameters in 5LWB, 4/8 in RMR, and 3/8 in SS7. Chronotype is independently associated with dual-task gait cost in a task-domain-specific manner, with stronger effects for phonological and sequential tasks than for arithmetic processing. The SS7 condition yielded the largest interference but weakest chronotype modulation, suggesting arithmetic dual-task disruption may be less sensitive to circadian arousal. Fixed testing time and cross-sectional design warrant within-subject, multi-timepoint studies to confirm chronotype effects separate from time-of-day confounds.

Background Inflammatory markers play an important role in the pathophysiology of Lumbar disc herniation (LDH). This study presents a comprehensive multi-assessment of the inflammatory landscape by combining serum inflammatory cytokines quantification, their diagnostic performance, associations with radiological features, and integrating the experimental findings into an in-silico protein-protein interaction network. Methods A multifaceted study design was utilized to quantify and compare the distribution of selected inflammatory cytokines in patients with LDH and control subjects. The diagnostic ability of these cytokines was assessed using receiver operating characteristic curve analysis. The cytokines values were correlated with selected radiological findings including disc herniation subtypes (protrusion, extrusion, and sequestration), and further categorized as contained and non-contained in patients using a Spearmans rank correlation test. Additionally, computational analysis was performed to identify the central hubs and functionally enriched pathways. Results In patients with LDH, IL-6 and IL-1{beta} showed statistically significant (IL-6: p < 0.001; IL-1{beta}: p = 0.001) rise, but IL-6 showed high diagnostic and discriminative power (AUC = 0.99; cut-off: 19.99 pg/mL). Further IL-1{beta} exhibited a positive correlation with non-contained disc herniation (extrusion and sequestration), while displaying a significant (p < 0.05) negative correlation with protrusion. In silico analysis identified IL-1{beta}, IL-8, TNF-, IL-6, IL-1, CSF2, CSF3, and IL-10 as central hubs, with IL-1{beta} being the top ranked hub in determining functionally enriched cytokine-cytokine receptor interaction. Conclusions Study confirmed IL-6 as a powerful diagnostic marker for LDH, while IL-1{beta} aids in determining contained and non-contained disc herniation. Further, IL-1{beta} was identified as the central hub, triggering functionally enriched pathways in the pathogenesis of LDH.

Endophytic fungi residing within medicinal plants are emerging as prolific sources of structurally diverse bioactive secondary metabolites with applications in drug discovery. Azadirachta indica (Neem) and Melia azedarach (Melia), members of the Meliaceae family, are renowned for their rich phytochemical composition; however, the contribution of their endophytic fungi communities to this chemical diversity remains largely unexplored. Herein, endophytic fungi were isolated from leaves and bark of Neem and Melia collected in Kenya and cultured under distinct physical conditions, solid (plates) and liquid (broth) media to assess how culture environment influences compound production. Compounds were extracted and analyzed using gas chromatography-mass spectrometry (GCMS) to profile the chemical diversity associated with each endophytic fungi, physical culturing state and host plant. GCMS analysis revealed that while the host plant identity influences the presence of specific compounds, the dominant determinant of chemical diversity was intrinsic biosynthetic capacity of the endophytic fungi themselves. Several compounds were unique to endophytic fungi cultures, highlighting their role as independent sources of bioactive compounds. Culture conditions moderately influence metabolite profiles, demonstrating the importance of optimizing growth environments in experimental design and natural product bioprospecting. From the Neem samples, we found 53 compounds uniquely present in the broth samples (consisting of Neem powder and endophytic fungi), 22 found exclusively with the endophytic fungi from the Neem, and 31 compounds shared between the broth and the endophytic fungi samples. In Melia samples, 109 compounds were uniquely present in broth samples from Melia plant (consisting of Melia powder and endophytic fungi), 22 compounds were found exclusively with the endophytic fungi from the Melia, and 55 were shared between the broth and the endophytic fungi samples. Our comparative analysis assessed the Neem and Melia endophytic fungi exclusive samples and reported 12 shared compounds. 10 compounds were unique to Neem and 10 unique to Melia; however, their identities varied between the two categories. While GCMS enabled the identification of volatile and semi-volatile metabolites, future studies employing complementary metabolomic approaches, such as liquid chromatography-mass spectrometry (LCMS), ultra-high-performance liquid chromatography MS/MS (UHPLC MS/MS), or nuclear magnetic resonance (NMR) spectroscopy, would expand coverage to non-volatile, polar, and high molecular weight compounds, providing a more comprehensive understanding of endophyte-derived chemical diversity. These findings provide insights into the interplay between medicinal plants and their endophytes and establish a foundation for leveraging endophytic fungi from Neem and Melia as scalable sources of structurally complex natural products for pharmaceutical and biotechnological applications while minimizing ecological impact.

PurposeColonic motility disorders, including diarrhea-predominant irritable bowel syndrome and slow-transit constipation, impose a major clinical burden. Although high-resolution colonic manometry reveals characteristic spatiotemporal motor patterns, such as high-amplitude propagating contractions and cyclic motor pattern in healthy individuals, these patterns are often altered or absent in disease. Understanding how these patterns arise from underlying pacemaker, neural, and mechanical mechanisms is essential for improving treatment strategies. MethodsWe developed a biophysical whole-colon model that integrates an Interstitial Cells of Cajal-inspired oscillator network, enteric nervous system reflexes, a pressure-gated modulation element motivated by rectosigmoid brake behavior, and a nonlinear tube law describing colon wall mechanics. The model simulates spatiotemporal pressure patterns along the colon and allows systematic variation of physiological parameters associated with pacemaker activity, neural reflex control, and distal gating. ResultsA small set of parameters reproduces three illustrative motility patterns corresponding to healthy motility, diarrhea-predominant irritable bowel syndrome, and slow-transit constipation. The simulated pressure maps recapitulate key features observed in high-resolution manometry, including propagation direction, regional patterning of contractions, and case-specific changes in amplitude and coordination. Sensitivity analysis suggests that proximal excitation strength and waveform morphology strongly influence global motility metrics. ConclusionOur study presents a simple, biophysical framework for reproducing clinically observed colonic motor patterns and exploring their disruption in disease. More broadly, the model may help interpret clinical manometry in mechanistic terms and support hypothesis-driven in silico studies of colonic motility disorders.

Wastewater surveillance is increasingly used for antimicrobial resistance (AMR) monitoring in urban environments, but low-resource settings often lack a piped sewerage system. Instead, coprophagous flies--flies that ingest feces--may serve as composite samplers for monitoring fecal wastes present in terrestrial environments. We evaluated whether the class 1 integron-integrase gene intI1 was associated with genetic markers of AMR and fecal source tracking markers (FST) in coprophagous flies collected from latrine entrances and food preparation areas in low-income urban Maputo, Mozambique. We quantified intI1, an enteric 16S rRNA target (for normalization), three FST markers, and 30 ARG targets using qPCR. We normalized concentrations of intI1 and each target to enteric 16S rRNA. We fit linear mixed models with a random intercept for housing compound to estimate within-fly associations between log10 relative abundance of intI1 and log10 relative abundance of each target with and without adjustment for fly taxonomic group, capture location, and standardized fly mass. We also modeled per-fly unique ARG count (i.e., number of ARG targets detected) using Poisson regression. Of 188 flies assayed, 176 passed internal controls; intI1 and enteric 16S rRNA were detected in 95% and 96% of flies, respectively. Higher relative abundance of intI1 was positively associated with ARG and FST targets, with the strongest associations observed for sulfonamide-(sul1: {beta} = 0.87; 95% CI: 0.81, 0.94; sul2: {beta} = 0.81; 95% CI: 0.73, 0.89), tetracycline- (tetA: {beta} = 0.78; 95% CI: 0.70, 0.85; tetB: {beta} = 0.69; 95% CI: 0.60, 0.79), and trimethoprim-related (dfrA17: {beta} = 0.78; 95% CI: 0.70, 0.86) genes. Associations with FST markers were weaker (i.e., human mtDNA: {beta} = 0.46; 95% CI: 0.37, 0.55; human-associated Bacteroides: {beta} = 0.34; 95% CI: 0.25, 0.43). Higher relative abundance of intI1 was also associated with a greater number of ARGs detected: each 10-fold increase in intI1 was associated with an 8% higher expected unique ARG count (aRR=1.08, 95% CI: 1.04-1.12). These findings support the need for further research across different settings exploring intI1 carried by coprophagous flies as a potential standardized screening target for AMR surveillance in unsewered terrestrial environments.

The retinal topography of mammals reflects significant influences of the visual environment. In diurnal species, local specializations, such as the visual streak (VS) for panoramic vision and the area centralis or fovea for binocular vision, play a key role in optimizing visual perception and species viability. While the location of these sites is typically considered the retinal center, the definition of a "central retina" is less clear in nocturnal species. In mice, the most frequently used model in ophthalmologic research, the location of a central retina is hardly discernible in retinal images, neither in retinal structure (OCT sections) nor in vascular organization (SLO and angiography). In this study, we compare the murine retina with that of a diurnal rodent, the Mongolian gerbil (MG). We found that the S-opsin transitional zone (OTZ), a region characterized by the change from S-to M-opsin dominance along the dorsoventral opsin gradient in mice, has a similar relative position in the retina to the VS in the Mongolian gerbil, suggesting an evolutionary positional homology between these regions. Further, since the S-opsin-dominant region is optimized for visualizing the sky and the M-opsin-dominant region for visualizing the ground, the OTZ in between -much like the VS- naturally points toward the horizon. We therefore propose considering the OTZ as the position of a "central retinal area" in mice. Determining the anatomical-physiological center is particularly important to obtain meaningful relative measures such as averages across different retinal areas, as the common referencing to the optic nerve head (ONH) in mice does not take into account retinal organization and the eccentric position of the functional center.