Chemical Senses

Background: Smell testing is increasingly recognized as essential in rhinology practice but remains underutilized because of time constraints and limited clinical resources. This study aimed to evaluate the performance (test-retest reliability, accuracy and test completion time) of a self-administered, digital version of SMELL-RS, a non-semantic test of olfactory resolution (SMELL-R) and sensitivity (SMELL-S). Methodology: We performed a test-retest reliability study in a tertiary care facility. We enrolled 100 subjects with and without smell dysfunction. The primary outcome measures were two replicates of olfactory test scores (SMELL-RS composite score, SMELL-R score, SMELL-S score). The secondary outcome measures were Sniffin Sticks score, test completion time, patient demographics, and other clinical characteristics (clinical symptoms, etiologies). Results: The SMELL-RS composite score was reliable (ICC=0.71; p<0.0001) and correlated with the Sniffin Sticks composite score (r=0.68; p<0.0001). Different etiologies have different magnitudes of smell loss as revealed by the SMELL-RS score. SMELL-S reduces misdiagnosis associated with Sniffin Sticks threshold tests. The average completion time of the olfactory resolution test (SMELL-R) was on average 5.9 minutes (SD=1.9), while the average completion time of the olfactory sensitivity test (SMELL-S) was 5.5 minutes (SD=2.7). This is two to three times faster than the corresponding Sniffin Sticks tests. Conclusions: SMELL-RS is a rapid, fully automated, reliable, and accurate olfactory test suitable for self-administration in a clinical setting.

Male odor induces various behavioral and physiological responses across the reproductive cycle in female mice. Although male odor preference in females is reduced during pregnancy, how it changes across later stages of the reproductive cycle, including nursing and weaning, remains unclear. Here, we found that male odor preference is lost during pregnancy and nursing. To identify the olfactory systems involved in these changes, we examined neural activity using c-Fos immunohistochemistry. Male odor exposure during nursing increased neural activity in the accessory olfactory bulb and the posteroventral medial amygdala (MeApv), a key node of the accessory olfactory system, as well as in subdivisions of the central amygdala, but not in the ventromedial hypothalamus or the bed nucleus of the stria terminalis. Finally, lesions of the MeApv prevented the loss of male preference during nursing, indicating that the MeApv is required for suppression of male preference during this stage.

There is a dearth of information on how different cocktails sweetened with different sugars impact brain activity. Glucose enters the brain faster and in greater concentration than fructose and directly affects neuronal activity of melanin-concentrating hormone (MCH) neurons. MCH signaling promotes both glucose drinking and alcohol intake by integrating central and sensory inputs, but it is currently unknown how MCH neuronal activity relates to sweetened cocktail drinking. This study sought to investigate the relationship between MCH activity and sugar-sweetened alcoholic cocktail drinking. We also sought to compare MCH neuronal responses to the sugar solutions without alcohol as well as their response to sensory stimuli. In female and male rats, we used fiber photometry to monitor MCH neurons in response to sensory stimuli and during drinking of 10% glucose, 10% fructose, and glucose or fructose cocktails with 1.25% or 10% alcohol. We found that MCH activity rises in response to a variety of sensory stimuli and peaks before the start of drinking for all cocktails, before returning to baseline near the start of drinking. The cocktail type impacted the dynamics of MCH activity, where increased alcohol concentration resulted in earlier MCH activity for fructose but not glucose cocktails. Finally, we found that peak MCH activity during drinking is correlated with approach behavior for all sugar and cocktail types. These findings suggest that glucose and alcohol may interact to directly influence MCH activity. Further, MCH neurons may regulate cocktail drinking in response to sugar type and alcohol concentration. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/719280v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@b992c3org.highwire.dtl.DTLVardef@1526895org.highwire.dtl.DTLVardef@1504c6dorg.highwire.dtl.DTLVardef@c990fc_HPS_FORMAT_FIGEXP M_FIG C_FIG New and noteworthyFiber photometry was used to monitor lateral hypothalamic melanin-concentrating hormone (MCH) neurons in male and female rats during sensory stimuli and drinking of glucose, fructose, or glucose- or fructose-sweetened alcoholic cocktails. Subsecond-scale changes in MCH activity occurred after stimuli. Peak MCH activity during drinking was correlated with approach behavior. Alcohol concentration only impacted MCH activity with fructose cocktails. We discuss the implications of MCH dynamics towards brain function, associative learning, and alcohol use disorder.

Equine asthma (EA) is a highly prevalent, chronic, inflammatory disease of the lower airways ranging from mild-to-moderate to severe clinical presentations. Diagnosis currently relies on bronchoalveolar lavage fluid (BALF) cytology, an invasive method associated with interobserver variability, which highlights the need for more reproducible approaches. MicroRNAs (miRNAs) are small noncoding RNAs involved in post-transcriptional gene regulation. They are stable and readily detectable in body fluids and have shown promising results as biomarkers in human asthma. The aim of this study was to characterize miRNA abundance profiles in BALF and serum from horses with distinct EA endotypes to evaluate their biomarker potential and explore their involvement in disease pathogenesis. A total of 43 horses were included and classified as either EA (n=32) or controls (n=11), based on clinical examination and BALF cytology. The EA horses were further divided into three endotypes based on BALF inflammatory cell composition: neutrophilic asthma (n=10), mastocytic asthma (n=15), and mixed asthma (n=7). RNA was isolated from both serum and BALF samples and analyzed by quantitative real-time PCR (qPCR) targeting 103 miRNAs linked to asthma and pulmonary inflammation in humans. Differential miRNA abundance was analyzed across EA endotypes. The most significantly differentially abundant miRNAs were used for in silico target prediction and pathway enrichment analyses. Horses with mixed EA had significantly lower levels of eca-miR-125a-3p and eca-miR-125b-5p in BALF compared to controls. Additionally, eca-miR-146a-5p abundance was significantly increased in BALF from horses with neutrophilic EA compared to mastocytic EA. Target and pathway enrichment analyses for eca-miR-146a-5p identified immune-relevant pathways, such as MAPK and T-cell receptor signaling, supporting its involvement in inflammatory processes associated with asthma. This study identified three promising candidates, eca-miR-125a-3p, eca-miR-125b-5p, and eca-miR-146a-5p, as potential biomarkers associated with different EA endotypes. These miRNAs are interesting candidates for further investigation in an independent cohort.

Introduction: Auditory brainstem response (ABR) is a standard objective method for estimating hearing threshold, especially in patients who cannot reliably participate in behavioral audiometry. However, ABR interpretation is usually performed by an expert. This study evaluated whether two general-purpose artificial intelligence (AI) multimodal large language model (LLM) chatbots, ChatGPT and Qwen, can accurately estimate ABR hearing thresholds from ABR waveform images. The accuracy was measured by comparisons with the judgements of 3 expert audiologists. Methods: A total of 500 images each containing several ABR waveforms recorded at different stimulus intensities were analyzed. Three expert audiologists established the reference auditory thresholds based on visual identification of wave V at the lowest stimulus intensity, with the most frequent judgment among the three used as the reference. Each waveform image was independently submitted to ChatGPT (version 5.1) and Qwen (version 3Max) using the same standardized prompt and without additional clinical context. Agreement with the expert thresholds was assessed as mean errors and correlations. Sensitivity and specificity for detecting hearing loss (>20 dB nHL) were also calculated. In cases where the AI and expert thresholds nominally matched, corresponding latency measures were also compared. Results: Auditory thresholds derived from both LLMs correlated strongly with expert opinion, with Pearson r = 0.954 for ChatGPT and r = 0.958 for Qwen. ChatGPT showed a mean error of +5.5 dB and Qwen showed a mean error of -2.7 dB. Exact nominal agreement with expert values was achieved in 34.6% of ChatGPT estimates and 35.6% of Qwen estimates; agreement within +/-10 dB was observed in 75.6% and 80.0% of cases, respectively. For hearing-loss classification, ChatGPT achieved 100% sensitivity but low specificity (20.4%), whereas Qwen showed a more balanced profile with 91.6% sensitivity and 67.5% specificity. Curiously, estimates of wave V latency were markedly poor for both LLMs, with systematic underestimation and weak correlations with the expert judgements. Conclusion: ChatGPT and Qwen demonstrated a moderate ability to estimate ABR thresholds from waveform images, although their performance was not good enough for independent clinical use. Both models captured general patterns of hearing loss severity, but there was systematic bias, limited specificity and sensitivity balance, and poor latency estimation. General-purpose multimodal LLMs may have potential as assistive or preliminary tools, but clinically reliable ABR interpretation will likely require specialized, domain-trained AI systems with expert oversight.

ObjectiveThe objective of this study is to assess the quality, empathy, and readability of large language model (LLM) responses regarding otologic questions from patients as they compare to verified physician responses in other patient-driven forums. This study aims to predict the potential utility of LLMs in patient-centered communication. Study DesignComparative study SettingsInternet MethodsA sample of 49 otology-related questions posted on Reddit r/AskDocs1 between January 2020 and June 2025 were selected using search terms including "hearing loss," "ear infection," "tinnitus," "ear pain," and "vertigo." Posts were retrieved using Reddits "Top" filter. Each question was answered by a verified doctor on Reddit and three AI LLMs (ChatGPT-4o, ClaudeAI, Google Gemini). Responses were scored by five evaluators. ResultsCommon otologic concerns posed in patient questions were otalgia (38.7%), vertigo (28.6%), tinnitus (24.5%), hearing loss (22.4%), and aural fullness (20.4%). LLM responses were longer than physician responses (mean 145 vs 67 words; p < .05) and rated higher in quality (10.95 vs 9.58), empathy (7.26 vs 5.18), and readability (4.00 vs 3.73); (all p < .05). Evaluators correctly identified AI versus physician responses in 89.4% of cases with higher sensitivity for detecting physician responses (93.5%). By Flesch-Kincaid grade level, ChatGPT produced the most readable content (mean 7.25), while ClaudeAI responses were more complex (11.86; p < .05). ConclusionLLM responses received higher ratings in quality, empathy, and readability than those of physicians in response to a variety of otologic concerns. When appropriately implemented, such systems may enhance access to understandable otologic information and complement clinician-delivered care.

Intraspecific variation is a prerequisite for natural selection and can manifest in various phenotypic traits, including vocal signals. However, classifying individuals based on their vocalizations, or acoustic individual identification (AIID), remains a significant challenge. This is particularly true for species that use rapidly varying echolocation calls for orientation. Here, we demonstrate that deep learning can overcome the limitation of traditional methods and reveal persistent individual signatures within bat echolocation calls. We recorded echolocation calls from 34 individuals of the greater leaf-nosed bat (Hipposideros armiger) under controlled laboratory conditions, with 19 individuals recorded repeatedly over three months. We show that a convolutional neural network (CNN) dramatically outperforms a traditional method, achieving an average identification accuracy of 84% for single calls and 91% for call sequences. In contrast, the traditional Discriminant Functional Analysis method achieved accuracies of only 39% and 47%, respectively. Through systematically altering the temporal structure of echolocation calls in input sequences, we found that temporal patterning enhances individual classification accuracy, suggesting it contributes to the encoding of individual-specific information. This study revealed that echolocation calls of H. armiger can contain stable, individual identity that were previously undetectable. Our findings highlight the potential of deep learning for non-invasive AIID and provide a methodological basis for future studies aiming to monitor animals in more dynamic environments.

Purpose: This research sought to develop a low-cognitive-load speech-in-noise test based on consonant confusions with the potential for assessing hearing-aid benefit. Methods: Vowel-consonant-vowel (VCV) stimuli with added speech-shaped noise were presented as a closed-set consonant identification task. Initially, consonant-confusion matrices were used to select, from a larger set of consonants and vowel contexts, a set of ten consonants and associated signal-to-noise ratios (SNR) that were sensitive to hearing loss. The sensitivity of the qVCV test to hearing loss was validated by comparing predicted pure-tone average (PTA) hearing thresholds with their audiometric PTA. Clinical viability of the qVCV test was assessed by comparisons to the QuickSIN test. Hearing-aid benefit was assessed by comparing test scores in unaided and aided conditions. Results: The consonants most sensitive to hearing loss were /b d g t k v z s [esh] n/ in the vowel context /[a]/. A cross-validated prediction of PTA had a mean-absolute error of 5.7 dB. The repeatability of qVCV at 50 trials was equivalent to the QuickSIN average of two lists. Hearing-aid benefit was quantified as a decibel reduction in hearing loss. Conclusions: qVCV and QuickSIN performed similarly when test times are equated. The advantages of qVCV include lower cognitive demand, fewer learning eeects, and automated scoring. PTA predicted by qVCV which greatly exceeds audiometric PTA may indicate either cognitive deficits or cochlear neural degeneration. The qVCV quantification of hearing-aid benefit may have clinical value

Conductive hearing loss (CHL) with a normal otoscopic exam can be difficult to diagnose because routine clinical measures such as audiometric air-bone gaps (ABGs) can identify a conductive component but often cannot distinguish among specific underlying mechanical pathologies (e.g., stapes fixation versus superior canal dehiscence, which may produce similar audiograms). Wideband tympanometry (WBT) is a fast, noninvasive test that can provide additional mechanical information across a broad range of frequencies (200 Hz to 8 kHz). However, WBT metrics are influenced by variations in ear canal geometry and probe placement and can be challenging to interpret clinically. In this study, we extend prior WBT absorbance-based classification work by estimating the middle ear input impedance at the tympanic membrane (ZME), a WBT-derived metric intended to reduce ear canal effects. To estimate ZME, we fit an analog circuit model of the ear canal, middle ear, and inner ear to raw WBT data collected at tympanometric peak pressure (TPP). Data from 27 normal ears, 32 ears with superior canal dehiscence, and 38 ears with stapes fixation were analyzed. A multinomial logistic regression classifier was trained using principal component analysis (retaining 90% variance) and stratified 5-fold cross-validation with regularization. We compared feature sets based on ABGs alone, ABGs combined with absorbance, and ABGs combined with the magnitude of ZME. The combination of ABGs and the magnitude of ZME produced the best performance, achieving an overall accuracy of 85.6% compared to 80.4% for ABGs alone and 78.4% for ABGs combined with absorbance. These results suggest that incorporating model-derived middle ear impedance features with standard audiometric measures (ABGs) can improve automated pathology classification for stapes fixation and superior canal dehiscence.

Characterizing aroma and flavours generated during cheese production is of high relevance for the food industry. A deeper comprehension of flavour generation can be achieved by understanding the role of microbial population governing milk processing, and in particular their metabolic activity governed by gene expression. In this work we considered two independent experiments in which gene expression of the microbial population involved in cheese processing is sampled, together with final volatile products quantification. We estimated the final volatile compound profile from the measured metatranscriptomic expression by using machine learning with two different strategies for model training and validation, and we were able to associate specific biochemical pathways to the identified gene signatures.

Transcriptomic analysis is considered a powerful approach for biomarker discovery, however still exploring large scale omics dataset to extract meaningful biological insights remains a challenge for biologists. To address this gap, we present ARACRA a fully automated RNA-seq analysis pipeline including entire transcriptomics workflow from raw FASTQ files to the transcriptomics Point of Departure (tPoD) with human-in-the-loop review process. Overall, the analysis is performed in two phases: Phase 1 carries out the acquisition of raw reads, pre-alignment quality control, alignment to reference genome and quantification of gene expression. Whereas, Phase 2 performs statistical analysis including Differential Gene Expression analysis and Dose-Response modelling. Two phases are separated by an extensive quality control step which allows the user to visually inspect the quality of data processed and helps in filtering noise and outlier samples. ARACRA facilitates end-to-end analysis of RNA-Seq data through an interactive web-based application developed on nextflow and streamlit for minimizing computational complexities while ensuring correct downstream processing. Availability and implementationARACRA is freely available online at the GitHub with MIT License and stream lit-based web application: ARACRA. Researchers can use the demo data or even upload their own data to do the analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/716912v1_fig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@15170a9org.highwire.dtl.DTLVardef@1bb9822org.highwire.dtl.DTLVardef@1010f3aorg.highwire.dtl.DTLVardef@8ee6e6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1:C_FLOATNO Overall Architecture of ARACRA C_FIG

The human auditory system decomposes complex sounds into distinct components via a collection of processing steps. Knowing whether Spiral Ganglion Cells (SGCs) play an active role in the decoding of complex sounds can facilitate the development of Cochlear Implant (CI) coding strategies and clinical assessment tools. Early animal studies reported SGCs being similar across different characteristic frequencies (CFs). In this study, human electrically evoked compound action potentials (eCAPs) were analysed to probe the relationship between the reciprocal of CF and the duration of the eCAP. A significant relationship could indicate that SGCs may not simply be passive cables. eCAP datasets from 6 published studies (175 CI users, 1243 recordings) were analysed and their peaks were automatically labelled. The n1p2 latency was derived for each recording as a proxy of the action potential duration. The CF of each recording was estimated by mapping the average insertion angle of the electrode to the human SGC map. A weak but statistically significant relationship was observed between the n1p2 latency and the reciprocal of CF (random-effects model with random intercepts for subject, r = 0.09, p = 0.024, n= 450) supporting the hypothesis that lower CF is associated with slower repolarisation (longer n1p2 latency) in human spiral ganglion cells.

Accurate species identification is crucial to assess the medical and veterinary relevance of a mosquito specimen, but it requires high experience of the observers and well-equipped laboratories. This study aimed to evaluate whether low-cost imaging in combination with geometric wing morphometrics can provide accurate identification of invasive, morphologically similar Aedes species. The right wings of 670 female specimens covering 184 Ae. aegypti, 156 Ae. albopictus, 166 Ae. j. japonicus and 164 Ae. koreicus, were removed, mounted and photographed with a professional stereomicroscope (Olympus SZ61, Olympus, Tokyo, Japan) and a macro lens (Apexel-24XMH, Apexel, Shenzhen, China) attached to a smartphone. The coordinates of 18 landmarks on the vein crosses were digitalized by a single observer for each image. In addition, the landmarks of 20 specimens per species and imaging device were digitalized by six different observers to assess the degree of the observer error. The superimposed shape variables were used to compare the species classification accuracy of linear discriminant analysis (LDA), support vector machine (SVM), Random Forest (RF), and XGBoost. In the single-observer landmark data, the LDA achieved the best classification results with a mean accuracy of 95 % for landmarks from microscope images and 92 % for those obtained from smartphone images. In the multi-observer landmark data, LDA consistently performed worse than the other three classifiers, and the reduction in the accuracy was more pronounced for smartphone images than for microscope images. This pattern was associated with a higher degree of observer error for smartphone images, as confirmed by a landmark-wise comparison across all landmarks. Geometric wing morphometrics provides a reliable method to distinguish the most common invasive Aedes species in Europe. Thereby, the image quality obtained by smartphones equipped with a macro lens is sufficient and represents a cost-effective alternative to professional microscopes. However, due to the greater degree of observer variation for smartphone images, landmark coordinates for such images should ideally be collected by a single observer.

BackgroundThe interpretation of high-dimensional transcriptomic data remains a major challenge in mechanistic toxicology and drug safety assessment. Conventional clustering approaches based solely on expression profiles often fail to capture intrinsic biological relationships among genes, limiting interpretability and downstream analysis. MethodsWe developed a hierarchy-aware gene exploration platform that integrates structured biological knowledge from the HUGO Gene Nomenclature Committee (HGNC). The core of the framework is a similarity kernel based on a single-step hyperdiffusion formulation (HKH{top}), which embeds gene family hierarchy into the similarity space. The platform is implemented as an interactive web application supporting Uniform Manifold Approximation and Projection (UMAP) visualization, Leiden clustering, functional enrichment analysis, and hierarchy-based gene recommendation. ResultsApplied to a transcriptomic dataset of acetaminophen-induced acute liver failure (APAP-ALF), the proposed approach achieved a 33.8-fold improvement in functional coherence compared to an expression-only baseline. The hierarchy-aware embedding produced compact and biologically consistent clusters, enabling identification of key toxicological modules, including disruption of RNA processing, extracellular matrix remodeling, and impairment of lipid transport. In addition, the framework detected small but highly significant regulatory modules associated with epigenetic reprogramming. ConclusionBy incorporating biological hierarchy into gene similarity, the proposed platform enhances the interpretability of transcriptomic analysis and enables structured exploration of functional relationships. This approach provides a practical framework for mechanistic insight generation and supports more transparent and reproducible analysis in toxicogenomics. AvailabilityThe web application is freely available at https://hgncgeneexplorer.streamlit.app/.

Background: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease associated with clinical, psychosocial, and economic burden. Accurate severity assessment is essential for guiding treatment escalation and monitoring disease activity, yet clinician-based scoring systems such as the Eczema Area and Severity Index (EASI) are limited by subjectivity and considerable inter- and intra-rater variability. Erythema, a key driver of AD severity grading, is particularly prone to inconsistent evaluation due to differences in ambient lighting, device quality, skin tone, and rater experience, underscoring the need for objective, reproducible assessment tools. Objective: To develop and validate an artificial intelligence (AI) pipeline for grading erythema, excoriation, and lichenification severity in AD from clinical photographs. The study evaluated the level of agreement between AI severity ratings in each category against dermatologists, non-specialists, and a consensus reference standard, with erythema as the primary outcome of interest. Methods: A two-stage AI pipeline was developed using EfficientNet B7 convolutional neural networks (CNNs). The first CNN was trained as a binary AD classifier on 451 AD and 601 non-AD images for lesion detection and segmentation. The second CNN was trained on 173 dermatologist-annotated AD images which were scored on a 0-3 ordinal scale for erythema, excoriation, and lichenification. This CNN had a downstream feature extraction algorithms such red channel contrast for erythema, Law's E5L5 for excoriation, and S5L5 texture maps for lichenification. In a cross-sectional validation study, 41 independent test images were scored by two blinded dermatologists and two blinded physicians. AI predictions were compared to individual rater groups and mode-derived consensus scores using weighted Cohen's kappa, classification accuracy, confusion matrices, and error direction analyses. Results: On internal validation, the severity CNN achieved 84% overall accuracy (averaged across all three attributes), 86% sensitivity, 87% specificity, and a macro-averaged area under the receiver operating characteristic curve (AUC) of 0.90. In the external comparison with blinded human raters, erythema agreement between the AI and dermatologist consensus was substantial (accuracy 80.7%; kappa = 0.68), with no large (>2-point) misclassifications. Physician consensus agreement was lower (accuracy 54.8%; kappa = 0.34), reflecting greater variability among primary care physicians (non-specialists). For excoriation, AI-dermatologist agreement was moderate (accuracy 72.4%; kappa = 0.62); for lichenification, agreement was similar (accuracy 71.4%; kappa = 0.59). Across all features, disagreements were predominantly between adjacent severity categories. The AI was able to generate erythema severity grades for images of darker skin tones that dermatologists typically would not rate and were marked as "unable to assess". Limitations: The validation set was small (41 images), severe cases (score 3) were underrepresented, one rater participated in both training annotation and validation scoring, and sample size was insufficient for robust stratification by skin tone or body site. Conclusion: The AI pipeline demonstrated dermatologist-level accuracy for erythema scoring, consistent moderate agreement for excoriation and lichenification, and a potential advantage in assessing erythema on darker skin tones. These findings support its potential as a standardized, objective tool for AD severity assessment. Prospective validation in larger, more diverse cohorts is warranted.

The relaxin-3/relaxin family peptide receptor 3 (RXFP3) neuropeptidergic system is emerging as a potential target for treating various neuropsychiatric diseases, particularly those involving dysregulated stress and arousal. RXFP3 is abundantly expressed in several hypothalamic nuclei, and in the zona incerta (ZI). These regions play a central role in the regulation of stress and arousal, however the function of relaxin-3/RXFP3 within these circuits is unknown. The purpose of this study was to begin characterising this function by describing the distribution and genetic signature of neurons that express RXFP3. We used RNAscope fluorescent in situ hybridisation to characterise the spatial expression pattern and neurochemical phenotype of cells expressing Rxfp3 mRNA throughout the mouse lateral hypothalamus (LH) and ZI. We found that Rxfp3 is expressed across the rostrocaudal extent of both the LH and ZI and follows a parabolic pattern of expression, peaking in more rostral areas of each nucleus. Neurochemical phenotyping of Rxfp3+ cells with Gad1, Slc17a6 (vGlut2), Pvalb, Th, and Sst showed that LH/ZI Rxfp3+ cells co-express each marker to varying extents, generally proportional to their overall abundance within each structure. Furthermore, LH/ZI Rxfp3+ cells overlapped with several known populations involved in various facets of fear learning and defensive behaviour, such as the dopaminergic A13 group, somatostatin-expressing rostral ZI neurons, and glutamatergic LH neurons. The neurochemical diversity of these neurons may reflect the overall role of both the LH and ZI as global regulators of behaviour and the role of relaxin-3/RXFP3 signalling in modulating high-vigilance states.

Pollen is a robust and widespread substance that captures a historical snapshot of a specific time and place, and it can be used to track movements through space by examining the pollen deposited on various objects. Palynology, the study of pollen, is used across fields such as conservation, natural history, and forensics, where it is particularly useful for tracing the origin and movement of objects. However, pollen has remained underutilized due to the difficulty of distinguishing many pollen taxa beyond the family level and limited pollen reference material to support location predictions. With recent developments in pollen DNA metabarcoding these issues have been rectified, but much of the available pollen data are primarily from wind-pollinated species, which are widespread and less informative of specific sample locations. Bee-collected pollen presents an untapped resource in training predictive models to geolocate sample origin. Here we compiled bee-collected pollen DNA sequence relative abundance data from three projects in the western U.S. and assessed the accuracy of supervised machine learning models to predict the location of sample origin based solely on pollen assemblage, without the need of incorporating additional data. Random Forest and k-Nearest Neighbors models yielded high accuracy across all projects. We also found that models trained on taxonomically clustered pollen assigned sequence variants (ASVs) performed slightly better than those trained on raw sequence data, but the difference was minor, indicating that models trained on raw sequence data can reliably predict location and avoid the time-consuming taxonomic assignment process. Our results demonstrate the utility of repurposing bee-collected pollen for geolocation and provide a framework for employing supervised machine learning in future geolocation efforts. HighlightsO_LIBee-collected pollen metabarcoding data was used to accurately predict sample origin C_LIO_LIRandom Forest and k-Nearest Neighbors algorithms were most accurate with lowest error C_LIO_LITaxonomically-classified and raw DNA sequence data training sets performed comparably C_LI

Background: Beaches are popular summertime destinations in Canada. However, they can be affected by specific fecal pollution sources, increasing the risk of recreational water illness. Objectives: This study was conducted to determine the risks of acute gastrointestinal illness (AGI) among Canadian beachgoers and to evaluate the influence of different fecal indicator bacteria (FIB) and other water quality measures on assessing these risks. Methods: In a prospective cohort design, beachgoers were recruited at sites across Canada from 2023 to 2025. Sociodemographic characteristics and exposures were determined through an on-site survey, with a 7-day follow-up survey to determine risks of AGI. Bayesian mixed-effects logistic regression models were fitted to evaluate the effects of an ordinal water contact variable (no contact, minimal contact, body immersion, and swallowed water) on the incident risk of AGI, with an interaction included for water quality indicators. The levels of six FIB and water quality measures were assessed: Escherichia coli, enterococci DNA, three microbial source tracking DNA markers (human HF183/BacR287, human mitochondria, seagull Gull4), and turbidity. Results: A total of 4085 participants were recruited, with 67.6% completing the follow-up survey. The overall incident risk of AGI was 2.6%. Both swallowing water and body immersion increased AGI risks compared to no water contact: median of 20 excess cases (95% Credible Interval [CrI]: 4, 64) and 5 excess cases (95% CrI: 1, 19) of AGI predicted per 1000 beachgoers, respectively. Escherichia coli and seagull DNA marker levels were associated with AGI among those who had water contact, particularly among those who reported swallowing water. Discussion: While the overall burden of AGI due to beach water contact in Canada was low, increased risks are associated with E. coli levels particularly among those who swallow water. This could be related to fecal contamination from seagulls. However, there is substantial uncertainty in the predicted effect sizes.

BackgroundKidney cell lines are widely used to model kidney physiology and disease; however, their gene expression profiles may differ from primary cells due to immortalization, culture conditions, or experimental treatments. Determining whether a cell line resembles its native cell type is critical for interpreting in vitro findings. We developed a transcriptome-based approach that matches bulk RNA-seq data from kidney cell lines, primary cells, or tissues to reference cell types derived from single-cell RNA-seq (scRNA-seq) datasets. MethodsReference transcriptomic profiles were generated from two human and two murine kidney scRNA-seq datasets by pseudobulk aggregation. Bulk RNA-seq data from microdissected kidney tissue, non-kidney negative controls, and kidney cell lines were matched to these references using three statistical similarity measures (Spearman correlation, Euclidean distance, Poisson distance) and three machine learning classifiers (Random Forest, XGBoost, TabPFN). Each was assessed with global gene expression, curated kidney marker gene lists, and the most variable genes. Matching accuracy was evaluated through a three-step validation strategy: within-dataset matching, cross-reference comparison, and validation against primary kidney tissue and negative controls. ResultsGene expression rank-based Spearman correlation and TabPFN, a foundation model for tabular data, emerged as the most accurate and specific approaches, particularly with curated kidney marker gene lists. Both methods correctly identified microdissected kidney tubule segments and were robust against non-kidney negative controls. Applied to commonly used kidney cell lines, OK cells retained proximal tubule identity, particularly under shear stress, while other proximal tubule lines (HK-2, HKC-8, HKC-11) showed inconsistent matching. Collecting duct-derived mIMCD-3 maintained stable similarity across passages, culture conditions, and genetic modifications. ConclusionWe provide two complementary implementations: CellMatchR, an accessible web-based tool using Spearman correlation for routine use, and comprehensive scripts for TabPFN-based matching (link will be added after peer reviewed publication). Together, these resources enable researchers to make informed decisions about kidney cell culture model selection, interpretation, and stability. Translational StatementKidney cell lines are fundamental tools in nephrology research, yet their transcriptomic similarity to native cell types is rarely validated systematically. We demonstrate that combining bulk RNA-seq data with single-cell reference datasets enables robust assessment of cell line identity using gene expression-rank-based correlation and machine learning approaches. By providing a comprehensive evaluation of matching methods, curated kidney marker gene lists, and reference datasets, our study serves as both a practical resource and a methodological framework for the kidney research community, facilitating informed selection of cell culture models, quality control of experimental conditions, developing new experimental cell culture models, and more reliable translation of in vitro findings to kidney physiology and disease.

Methods: Using Global Burden of Disease (GBD) data, we analyzed prevalence, incidence, mortality, and disability-adjusted life years (DALYs) rates across global and 21 GBD regions from 1990-2023. Joinpoint regression identified temporal trends, age-period-cohort models analyzed effect contributions, Das Gupta decomposition quantified demographic and epidemiological impacts, inequality indices assessed health equity, and Bayesian models projected 2024-2038 trends. Results: In 2023, the global number of children and adolescents with asthma reached 131 million, with an age-standardized prevalence rate (ASPR) of 1,789.9 per 100,000. From 1990 to 2023, the global ASPR and age-standardized incidence rate (ASIR) of asthma in children and adolescents showed an upward trend, while the age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years (DALYs) rate (ASDR) exhibited a downward trend. Among the 0-14 age group, the disease burden was greater in males than in females, whereas in the 15-19 age group, males had a lower disease burden than females. Projections indicate that over the next 15 years, the overall disease burden will continue to decline; however, female mortality rates and DALYs rates are projected to show an upward trend. Conclusions: The increasing prevalence and incidence rates, coupled with declining mortality and DALYs rates of asthma among children and adolescents globally, underscore the necessity for targeted public health interventions. These findings provide crucial insights for early diagnosis, treatment optimization, and global health policy formulation.