Chemical Senses

BackgroundAuditory steady-state responses (ASSRs) provide an objective method for estimating hearing thresholds in individuals unable to provide behavioural responses. Bone conduction (BC) testing is required to differentiate conductive from sensorineural hearing loss. Accurate BC ASSR threshold estimation relies on "correction" factors, which are not yet well established. This meta-analysis evaluated the reliability of BC ASSR thresholds to estimate hearing thresholds at 500, 1000, 2000 and 4000 Hz. MethodsA systematic search of PubMed, the Cochrane Library, and Embase was conducted to identify studies involving normal-hearing (NH) and hearing-impaired (HI) participants of all ages. Outcomes were (1) the difference between ASSR behavioural and ASSR thresholds, and (2) ASSR thresholds. The risk of bias was evaluated using the Newcastle-Ottawa Scale. The mean and 95% confidence intervals (CI) were calculated for the thresholds at the four frequencies. The certainty of the evidence was assessed using GRADE approach. ResultsOf records identified, 11 records met the inclusion criteria, yielding a total of 27 studies. Sample sizes ranged from 60 to 249 participants across frequencies and age groups. The quality of records ranged from low to high. Data were synthesised using random-effects models due to heterogeneity. In NH adults, the mean differences ({+/-}95% CI) between BC ASSR thresholds and behavioural thresholds were 17.0 ({+/-}4.8), 15.5 ({+/-}6.0), 13.4 ({+/-}3.3), and 12.1 ({+/-}4.1) dB at 500, 1000, 2000, and 4000 Hz, respectively. In NH infants, mean ({+/-}95% CI) BC ASSR thresholds were 17.2 ({+/-}2.2), 10.5 ({+/-}3.6), 26.4 ({+/-}2.7), and 19.9 ({+/-}4.0) dB HL at the same frequencies. The certainty of the evidence was very low. ConclusionsBC ASSR can be a reliable method for estimating BC thresholds. However, age and frequency significantly impact BC ASSR thresholds, highlighting the need to develop of "correction" factors to accurately predict BC behavioural thresholds. RegistrationPROSPERO CRD42023422150.

Identification of early interventions to reduce/eliminate asthma - the most common chronic disease among children - could significantly reduce burden on the healthcare system. Large-scale asthma Exposome-Wide Association Studies (ExWAS) could identify potential interventions, however integration of diverse data is required to address association confounders. The CHILD Cohort Study has followed 3,454 healthy Canadian children and their families from early pregnancy, collecting exceptionally diverse data including 24,852 variables from participant questionnaires, clinical data, household and neighbourhood-level exposures, and sample-derived chemical analytic/omic datasets. Here, we report integration of these datasets into the CHILDdb database platform, and use these data to perform ExWAS and machine learning analyses, identifying and further characterizing associations between childhood asthma and 2,954 diverse early exposures (pregnancy to age 5). Significant asthma associations include antibiotic use, human milk components, DEHP phthalate, and mothers prenatal cleaning product/disinfectant exposure. Subsequent analysis revealed epigenetic changes in the cord blood at birth, after prenatal cleaner exposure, and different microbiome and/or inflammatory cytokine changes associated with different asthma-associated exposures in the child. Collective results support asthma as a heterogeneous condition involving multiple etiologies, with associated endotypes, including prenatal exposures with potential transgenerational effects, and suggest targets for early interventions.

BackgroundFatal insulin intoxication remains difficult to diagnose because insulin undergoes rapid degradation after death, limiting the reliability of direct biochemical measurements. This creates diagnostic uncertainty when objective molecular confirmation of insulin excess are required. We hypothesised that insulin excess induces systemic metabolic alterations that persist beyond insulin degradation and can be captured using postmortem metabolomics in a forensic setting. MethodsHigh-resolution mass spectrometry (HRMS)-based metabolomics was applied to a national cohort comprising 51 fatal insulin intoxications. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were trained on cases collected between 2017-2022 to identify insulin-associated metabolite features using a shared-and-unique-structures approach. Performance was evaluated using two temporally distinct test sets (2023-2024): a matched validation cohort and a heterogeneous forensic cohort reflecting biological variability. ResultsHere we show that an insulin-associated metabolomic fingerprint comprising 91 features demonstrated reproducible discrimination across independent cohorts. In the matched cohort (n=59, including 14 insulin cases), insulin intoxication classification achieved 100% sensitivity and 73% specificity within the applicability domain. In the heterogeneous cohort (n=154, including 14 insulin cases), 100% sensitivity was maintained with a 72% specificity despite increased biological variability. Univariate analyses demonstrated significant alterations across multiple metabolite classes, including acylcarnitines, fatty acids/lipids, and purine/nucleoside metabolites, with moderate effect sizes, consistent with systemic effects of insulin-induced hypoglycaemia. ConclusionsFatal insulin intoxication is associated with a reproducible metabolomic fingerprint detectable after death. These findings demonstrate that postmortem metabolomics may serve as a complementary decision-support tool when conventional biomarkers are unreliable.

ObjectiveThis study aimed to characterize the activation of lower urinary tract (LUT) targets in response to pudendal nerve stimulation (PNS) in awake human participants. Materials and MethodsIn this single center study, recruited participants had an implanted pudendal neurostimulator for treatment of their symptoms including overactive bladder, incontinence, urinary retention, and/or pelvic pain. Participants came in for a modified urodynamic study where a multichannel manometry catheter was placed in the lower urinary tract alongside a dual sensor urodynamics catheter. The bladder was filled and after each participant expressed a strong desire to void, PNS was applied and LUT pressures were measured. Participants attempted voids with the catheters in place to characterize LUT behavior and voiding efficiency with and without stimulation. ResultsThe study consisted of 15 participants including 13 women. Across 133 total trials contractions were observed at the distal urethra 52 times (39%) and at the proximal urethra 46 times (35%). The maximum observed pressure change occurred significantly more often at the proximal urethra than the distal urethra (p = 0.007). There was a significantly higher maximum tolerable stimulation amplitude for low frequency stimulation (2-3.1 Hz) when compared to high frequency stimulation (30-33 Hz) (p = 0.041). In one participant there were four instances of stimulation driven bladder contractions with an average pressure change of 24.3 cmH2O (standard deviation = 10.5). There was not a significant difference in voiding efficiency or maximum flow rate with and without stimulation (p = 0.76 and p = 0.45, respectively). ConclusionsPNS can affect LUT pressures at tolerable stimulation amplitudes. The absence of an effect of PNS on voiding characteristics suggests a similar mechanism of action as sacral neuromodulation.

Infectious disease forecasts can inform public health decision-making. Wastewater monitoring is a relatively new epidemiological data source with multiple potential applications, including forecasting. Incorporating wastewater data into epidemiological forecasting models is challenging, and relatively few studies have assessed whether this improves forecast performance. We present and evaluate a semi-mechanistic wastewater-informed forecasting model. The model forecasts COVID-19 hospital admissions at the state and territorial levels in the United States, based on incident hospital admissions data and, optionally, SARS-CoV-2 wastewater concentration data from multiple wastewater sampling sites. From February through April 2024, we produced real-time wastewater-informed COVID-19 forecasts using development versions of the model and submitted them to the United States COVID-19 Forecast Hub ("the Hub"). We then published an open-source R package, wwinference, that implements the model with or without wastewater as an input. Using proper scoring rules and measures of model calibration, we assess both our real-time submissions to the Hub and retrospective hypothetical forecasts from wwinference made with and without wastewater data. While the models performed similarly with and without the wastewater signal included, there was substantial heterogeneity for individual locations and dates where wastewater data meaningfully improved or degraded the models forecast performance. Compared to other models submitted to the Hub during the period spanned by our submissions, the real-time wastewater-informed version of our model ranked fourth of 10 models, with the hospital admissions-only version of our model ranking second out of 10 models. Across the 2023-2024 winter epidemic wave, retrospective forecasts from wwinference would have performed similarly with and without the wastewater signal included: fifth and fourth out of 10 models, respectively. To better understand the drivers of differential forecast performance with and without wastewater, we performed an exploratory analysis investigating the relationship between characteristics of the input data and improved and reduced performance in our model. Based on that analysis, we identify and discuss key areas for further model development. To our knowledge, this is the first work that conducts an evaluation of real-time and retrospective infectious disease forecasts across the United States both with and without wastewater data and compared to other forecasting models. Author SummaryWastewater-based epidemiology, in combination with clinical surveillance, has the potential to improve situational awareness and inform outbreak responses. We developed a model that uses data on the pathogen concentration in wastewater from one or more wastewater treatment plants in combination with hospital admissions to produce short-term forecasts of hospital admissions. We produced and submitted forecasts of 28-day ahead COVID-19 hospital admissions from this model to the U.S. COVID-19 Forecast Hub during the spring of 2024 and found that it performed well in comparison to other models during that limited time period. To assess the added value of incorporating wastewater data into the model and to investigate how it would have performed had we submitted it during the entire 2023-2024 winter epidemic wave, we performed a retrospective analysis in which we produced forecasts from the model with and without including wastewater data, using data that would have been available in real-time as of each forecast date. Both versions of the model would have been median overall performers had they been submitted to the Hub throughout the season. When comparing the models performance with and without wastewater data included, we found that overall forecast performance was very similar, with wastewater data slightly reducing overall average forecast performance. Within this result, there was significant heterogeneity, with clear instances of wastewater data improving and detracting from forecast performance. We used trends in the observed data to generate hypotheses as to the drivers of improved and reduced relative forecast performance within our model. We conclude by suggesting future work to improve the model and more broadly the application of wastewater-based epidemiology to forecasting.

RationaleObstructive sleep apnea (OSA) is linked to cardiovascular, metabolic, and cognitive morbidity. Although COVID-19 has been associated with long-term respiratory and neurological sequelae, its role in precipitating new-onset OSA remains unclear. ObjectivesTo evaluate whether SARS-CoV-2 infection increases risk of developing OSA up to 4.5 years post-infection and how risk varies by hospitalization status, demographics, comorbidities, and vaccination status. MethodsThis retrospective cohort study used electronic health records from the Montefiore Health System in the Bronx. Adults tested for SARS-CoV-2 between March 1, 2020, and August 17, 2024, were classified as hospitalized COVID+, non-hospitalized COVID+, or COVID-. Patients with prior OSA or inadequate follow-up were excluded. Inverse probability weighting adjusted for demographic, clinical, socioeconomic, and vaccination covariates. New-onset OSA was assessed using weighted Cox proportional hazards models. Secondary outcomes including hypertension, myocardial infarction, heart failure, stroke, arrhythmia, pulmonary hypertension, type 2 diabetes, and obesity were evaluated with Poisson regression. Sensitivity analysis used a pre-pandemic control cohort. ResultsAmong 910,393 eligible patients, hospitalized [HR 1.41 (95% CI 1.14-1.73)] and non-hospitalized [HR 1.33 (95% CI 1.22-1.46)] COVID+ patients had higher adjusted risk of new-onset OSA versus COVID- controls. Similar findings were observed using historical controls (n=621046). After OSA onset, hospitalized COVID+ patients had higher risks of heart failure and pulmonary hypertension, while non-hospitalized COVID+ patients had higher risk of obesity vs COVID- patients. ConclusionsSARS-CoV-2 infection is independently associated with increased risk of new-onset OSA. These findings support targeted screening in post-COVID populations.

AimsGestational diabetes mellitus (GDM) is the most common pregnancy-related medical complication. GDM is linked to aberrant immune responses in both mothers and offsprings, specifically, the subsequent development of inflammatory diseases. Whereas prior research has focused on specific immune cell subsets, a comprehensive overview of the impacts of GDM on maternal and fetal immune landscape is lacking. Here, we aim to comprehensively decipher how GDM modulates various immune cell populations in mothers and offsprings. MethodsA prospective, longitudinal case-control study was carried out. Maternal blood from GDM-affected (GDM, n=18) and non-GDM-affected (Ctrl, n=21) mothers were collected at ante-(36-38 weeks of gestation) and post-partum (6-8 weeks post-partum) timepoints. Cord blood from GDM (n=7) and Ctrl (n=11) pregnancies were collected upon C-section. They were analyzed with the state-of-the-art cytometry by time of flight (CyTOF) with a 40-marker panel. Additionally, a publicly available RNA-seq dataset for cord blood mononuclear cells was re-analyzed to confirm results from CyTOF experiments. ResultsCompared to Ctrl, GDM was associated with more activated maternal T cell subsets ante-partum, including increased CD45RO+ and Ki67+ CD4+ T cell populations, which reverted post-partum. GDM-affected maternal innate lymphoid cell (ILC) also exhibited increased granzyme B production ante-partum. On the other hand, in GDM-impacted cord blood, fetal T and B cells were more activated, displaying less naive and more effector phenotypes, further supported by RNA-seq analyses. ConclusionsOur comprehensive analyses revealed that GDM is linked to profound changes in the immune landscapes of the mothers (ante-/post-partum) and foetuses (at birth), casting novel insights towards GDM pathophysiology. Longitudinal immune profiling might be warranted for early detection and stratification of risk, and development of targeted interventions to prevent inflammatory disorders in GDM mothers and their offspring. Research in contextO_LIWhat is already known about this subject? O_LIThe maternal and intrauterine environments are important contributors to long-term health outcomes of mothers and offsprings. C_LIO_LISome maternal and fetal immunity changes have been observed in gestational diabetes mellitus (GDM)-affected pregnancies. C_LIO_LIGDM is associated with increased risk of later-life metabolic and inflammatory diseases in mothers as well as offsprings. C_LI C_LIO_LIWhat is the key question? O_LIWhat are the longitudinal alterations in maternal and fetal immune landscapes in GDM-affected pregnancies? C_LI C_LIO_LIWhat are the new findings? O_LIHigh-dimensional immune profiling provided the most comprehensive overview of alterations in maternal and fetal immune landscapes associated with GDM. C_LIO_LIGDM is associated with skewing of maternal CD4+ T cell and ILC towards activated phenotypes ante-partum. C_LIO_LIGDM is linked to more activated fetal T and B cell profiles. C_LI C_LIO_LIHow might this impact on clinical practice in the foreseeable future? O_LIUnderstanding the complex alterations in the maternal and fetal immune landscape in GDM-affected pregnancy provides insights into the long-term impacts of GDM on the mother and offspring. C_LI C_LI

Poor sleep is linked to consumption of sugary foods/beverages and high neural responsivity to palatable food cues. Yet, whether hedonic liking for sweet taste explains these associations remains unclear. We examined cross-sectional associations of five sleep traits (chronotype, sleep duration, insomnia frequency, snoring, daytime dozing) and a composite sleep score with sweet food liking, and total and free sugar intake in 76,734 UK Biobank participants (39-72 years, 56.3% female). Models adjusted for age, sex, ethnicity, socioeconomic deprivation, and body mass index (Bonferroni-corrected =0.0025). Evening chronotype, more frequent insomnia and daytime dozing, and lower composite sleep score were associated with higher sweet food liking. Associations with intake were stronger for free than total sugar. Evening chronotype was associated with higher free sugar intake (g/day: {beta}=1.523, 1.309-1.737; g/1000 kcal: {beta}=0.450, 0.361-0.538), and daytime dozing showed a dose-response (dozing often vs never/rarely: g/day {beta}=6.307, 4.631-7.983). Snoring was associated with higher absolute (but not energy-adjusted) free sugar intake. A healthier sleep score was associated with lower free sugar intake (g/day {beta}=-2.193 [-2.464 to -1.922]; g/1000 kcal {beta}=-0.691 [-0.804 to -0.579]) but higher energy-adjusted total sugar intake ({beta}=0.633 [0.485-0.781]). Mediation analyses indicated sweet liking accounted for 15%-91% of several sleep trait and free sugar intake associations (indirect effects p<0.001). Poorer sleep health, particularly evening chronotype and daytime sleepiness, was associated with greater sweet liking and higher free sugar intake, with sweet liking partially mediating associations between sleep traits and sugar consumption. Sweet-taste liking may represent an underexamined pathway linking sleep/circadian disruption to free sugar intake.

PuhrposeTo evaluate the short- and long-term cross-sectional associations between COVID-19 infection and multidimensional sleep health. MethodsData from the COVID-19 Outbreak Public Evaluation (COPE) initiative were used to examine the association between a novel multidimensional sleep health measure (COPE Multidimensional Sleep Health Scale, CMSHS) modeled from the RuSATED instrument and (1) COVID-19 infection and (2) post-acute sequelae of SARS-CoV-2 infection (PASC). ResultsData from 11,326 respondents were used for this study. The cohort was comprised of 51% women, 61% non-Hispanic White, and 17% Hispanic adults. COVID-19 infection was more prevalent among participants who had not received a booster vaccination (55.4% vs. 30.2%, p<0.001); the number of comorbid conditions was higher among those who had been infected (2.2% vs. 1.7%, p<0.001). Participants with COVID-19 infection had significantly lower CMSHS scores indicative of worse sleep health compared with uninfected participants (3.52 {+/-} 1.37 vs. 3.78 {+/-} 1.30; p < 0.001). Participants with PASC had lower CMSHS scores in comparison to those without PASC (2.72 {+/-} 1.30 vs. 3.82 {+/-} 1.28, p<0.001). In adjusted models, a progressive decline in CMSHS scores was observed over 12 months following infection (3.52 {+/-} 0.05 vs. 2.98 {+/-} 0.04; p < 0.001 for <1 month vs. 6-12 months). ConclusionCompared with uninfected individuals, multidimensional sleep health was worse among persons who had a COVID-19 infection. Individuals with PASC had greater and persistent reductions in sleep health for up to 12 months post-infection. Brief summaryO_LISeveral studies have examined the negative effects of COVID-19 on sleep, however the effects of COVID-19 infection on multidimensional sleep health remain poorly understood as do these associations over time. Using a large, population-based cohort, this study evaluates short- and long-term effects of Covid-19 infection on overall sleep health. C_LIO_LIThe study provides evidence that COVID-19 infection is associated with impairments in overall sleep health, with effects persisting up to 12 months post-infection. The findings in this study demonstrate that poor sleep health is an important long-term consequence of COVID-19 infection and emphasizes the need for sleep assessment among patients affected by COVID-19. C_LI

ObjectiveAs cannabis use has increased in the United States, so has cannabinoid hyperemesis syndrome (CHS), a disorder characterized by severe nausea, vomiting, and abdominal pain among heavy cannabis users. We previously showed that CHS symptoms are associated with several behavioral and psychological characteristics linked to psychosocial impairment. We examined links between CHS symptoms and suicidal thoughts, behaviors, and proximal suicide risk factors. MethodsWe used data from the National Firearms, Alcohol, Cannabis, and Suicide survey, a nationally representative survey of 7,034 US adults. Items assessed symptoms of CHS and suicidal thoughts and behaviors. Comparisons focused on: those with daily cannabis use and CHS symptoms (n = 191), those with daily cannabis use without CHS symptoms (n = 882), those with past year cannabis use but not daily use (n = 1288), and those without past year cannabis use (n = 4673). ResultsThose with CHS symptoms reported the highest prevalence of suicidal thoughts and behaviors with most lifetime rates being significantly higher than those with daily cannabis use without CHS symptoms. Those with CHS symptoms also reported higher mean-levels of thoughts and feelings associated with suicide (i.e., perceived burdensomeness, thwarted belongingness, defeat, entrapment) than all the other groups. ConclusionsThose with CHS symptoms reported especially high rates of suicidal thoughts, behaviors, and attempts even when compared to others with daily cannabis use. People with CHS symptoms appear to be at high risk of suicide, possibly related to distress from their gastrointestinal symptoms and psychiatric, substance use, and medical comorbidities.

ObjectiveThis study explored the recovery experiences of individuals who report having (largely) recovered from long covid and who attributed their improvement to mind-body approaches. Design, setting and participantsWe conducted an explorative qualitative study using purposive recruitment through social media and snowball sampling. Eighteen adult women (aged 37-62 years), who self-identified as having had long covid and having substantially recovered through mind-body approaches participated in semi-structured interviews. Data were analysed using Saunders practical thematic analysis. ResultsDespite variation in personal narratives, a common trajectory emerged: participants moved away from a biomedical explanatory model towards one centred on nervous system dysregulation. This shift, sometimes following initial scepticism, was often described as a turning point, sparking hope and motivation to engage in self-directed strategies. Recovery was not linear but an iterative process, involving cycles of practice, reflection (especially when progress stagnated) and adaptation of mind-body techniques. Over time, participants gained insights into contributing factors and, in many cases, made intentional life changes to support ongoing recovery. These patterns echo findings from previous research on mind-body approaches in chronic pain and chronic fatigue, and align with neuroscientific perspectives on symptom generation. Most participants navigated this process without formal clinical support, relying instead on online communities and actively avoiding sources of (biomedical) information that conflicted with their new understanding. ConclusionsWhile causal inferences cannot be drawn from qualitative data, this study highlights potential mechanisms that may underpin recovery for people with long covid using mind-body approaches. Further research is needed to develop structured interventions, and to evaluate their efficacy and safety. Future research should also explore how prevailing narratives within healthcare and society influence treatment engagement and recovery trajectories. STRENGTHS AND LIMITATIONS OF THIS STUDYO_LIThis is the first study exploring experiences of recovery from long covid using mind-body approaches. C_LIO_LIIn-depth, real-world accounts capture the lived-experiences over time and allow in-depth exploration if the recovery process, while the semi-structured design facilitates the emergence of themes rarely captured in clinical research. C_LIO_LIGeneralisability is limited due to self-identified long covid status, lack of formal diagnostic verification, absence of strict definitions of mind-body approaches and recovery, and a relatively homogenous sample (mostly highly educated women). C_LI

Wastewater-based epidemiology provides a low-cost, scalable view of community infection dynamics, but converting these signals into actionable epidemiological insights remains difficult. Mechanistic models offer interpretability, yet, assumptions such as a constant transmission rate limit realism over long simulation horizons and heterogeneous settings. We present a susceptible-exposed-infectious-recovered (SEIR) universal differential equation (UDE) that links wastewater viral loads to case counts and embeds neural networks to represent time-varying parameters. Parameter and prediction uncertainties are quantified using an ensemble method. We assessed the method using newly collected data for Bonn, Germany, as well as published data for five cities in Rhineland-Palatinate, Germany. The proposed approach produces realistic out-of-sample projections of case counts over an up to 50-week test horizon, and it learns city-specific mappings to prevalence that generalise within each location. Compared to SEIR models with fixed transmission rates, the UDE captures non-stationary drivers (policy, behaviour, seasonality) without sacrificing epidemiological structure, while propagating observation and model uncertainty into the projections. Accordingly, the approach facilitates a scalable interpretation and exploitation of wastewater data for the monitoring of infectious diseases.

BackgroundCannabinoid hyperemesis syndrome (CHS) is characterized by episodes of severe nausea, vomiting, and abdominal pain among those with heavy cannabis use. We estimated differences between those reporting CHS symptoms and other daily and less frequent cannabis users on drug use, psychiatric problems, other health problems, antisocial behavior, and personality. MethodsThe National Firearms, Alcohol, Cannabis, and Suicide survey was administered to 7034 US adults in 2025. Survey items assessed substance use, common psychiatric symptoms, personality traits, and symptoms of CHS. ResultsThose with CHS symptoms reported the highest rates and greatest variety of drug use compared to others who used cannabis. Those with CHS symptoms reported higher rates of other drug use than those who used cannabis daily without CHS symptoms across a variety of drug classes, including opioids, hallucinogens, and sedatives, higher rates of drug overdoses, and greater use of all drug classes than those with less-than-daily cannabis use. Those with CHS symptoms also reported more depression, anxiety, sleep problems, chronic pain, antisocial behavior, intimate partner violence, and disinhibited personality traits than those who used daily (mean d = 0.58) and less frequently (mean d = 0.69) and those with no cannabis use in the past 12 months (mean d = 0.99). ConclusionsThose with CHS symptoms exhibit a variety of psychological and behavioral problems including higher rates of other drug use, psychiatric symptoms, antisocial behavior, and dysfunctional personality traits. Results highlight the importance of understanding and addressing the broader psychosocial challenges faced by people experiencing CHS symptoms. Highlights O_LICHS symptoms are linked to greater polysubstance use and overdose risk C_LIO_LICHS symptoms are associated with depression, anxiety, sleep, and pain problems C_LIO_LICHS tied to antisocial behavior and intimate partner violence C_LIO_LICHS shows disinhibited personality traits and low well-being C_LIO_LINational survey identifies high-risk psychosocial CHS profile C_LI

Hepatitis E virus (HEV) is considered a predominantly foodborne pathogen in developed settings. During COVID-19 lockdown periods, however, HEV concentrations in wastewater at a treatment plant in Munich, Germany decreased, suggesting that pandemic-related behaviour changes inadvertently influenced transmission. In contrast, reported cases and wastewater data from a smaller catchment showed no comparable decline. To assess whether the observed reduction is compatible with a near-exclusively foodborne infection and to reconcile the contrasting signals across surveillance modalities, we developed a stochastic, individual-level model of HEV transmission, shedding, and ascertainment in Munich. Using Approximate Bayesian Computation, we calibrated this model to wastewater and case data from 2020-2023, first separately and then jointly. Posterior parameter estimates indicated a substantial decline in transmission during lockdowns to about 35-40% of the non-lockdown level, with the 95% credible interval entirely below 1 (no change). Joint inference suggested that possible modest lockdown-associated increases in diagnosis probabilities and higher measurement variability in the smaller catchment masked this effect in clinical and small-scale wastewater data, respectively. These findings demonstrate how wastewater-based surveillance, used alongside reported cases, can enable more robust parameter inference for models of under-reported pathogens like HEV, thereby supporting informed public health risk assessments.

PurposeTo develop and validate a prediction model for sleep apnea syndrome (SAS) treated with continuous positive airway pressure (CPAP) in the general population. MethodsUsing claims and health checkup data held by JMDC Inc., linked to personal health records (Pep Up), we developed and internally validated a prediction model for SAS treated with CPAP, defined as a diagnosis of SAS and reimbursement records of CPAP. Every three months from January 1, 2022 to July 1, 2024 (i.e., 11 timepoints), we identified eligible individuals with available data both 1 year before and 1 year after that timepoint to define the presence/absence of SAS treated with CPAP, as well as 279 predictor variables. We developed a LightGBM model for the training and tuning datasets and evaluated its performance on the validation dataset. ResultsAmong 18,692,873 observations (mean age 44.8{+/-}11.3 years, women 37.5%) obtained from 1,858,566 people, 300,868 (1.6%) had SAS treated with CPAP. The area under the receiver operating characteristic curve was 0.898 (95% confidence interval 0.895-0.901). The positive predictive values among people with the top 1% and 10% prediction scores were 28.3% and 10.3%, respectively. According to the SHapley Additive exPlanations plot, male sex was the most important predictor, followed by age, body mass index, and waist circumference. We also demonstrated that personal health records significantly improved the predictive performance. ConclusionWe developed a prediction model to identify people at high risk of SAS and encourage them to undergo polysomnography or related tests.

IntroductionObesity and related health conditions are unevenly distributed across neighborhoods, often co-occuring with multiple health challenges and socioeconomic disadvantages. Using an ecosyndemic framework, which integrates ecological and social dimensions that contribute to the clustering of health problems, this study examines how adverse obesity-related health outcomes spatially cluster in relation to obesogenic environments and socioeconomic position (SEP) across Dutch neighborhoods. MethodsNationwide neighborhood-level data on health outcomes, obesogenic environmental exposures (food environment, walkability, drivability, bikeability, sports facilities), and SEP were combined for all inhabited Dutch administrative neighborhoods in 2016 (N=12,420). Cluster analysis was used to identify distinct neighborhood profiles and descriptive statistics to characterize each cluster, with spatial patterns visualized using an interactive heatmap and principal component plots. ResultsFive neighborhood clusters were identified. The Ecosyndemic cluster (N=1,070 neighborhoods) exhibited the highest burden of obesity (17% [IQR 16;19), chronic diseases (36% [IQR 33;38%) and risk of anxiety/depression (55% [IQR 51;58]), unhealthy food environments and low SEP. In contrast, the Privileged cluster (N=6,425) had more favorable health outcomes and living conditions, including lower obesity prevalence (12% [IQR 11;14]). The Psychosocial Vulnerability cluster (N=991) was notable for elevated risk of anxiety/depression (47% [IQR 43;51]) combined with relatively low obesity (11% [IQR 8;12]). The Syndemic cluster (N=1,836; obesity 15% [IQR 14;17]) and Towards Privileged cluster (N=2,098; obesity 12% [IQR 10;13]) represented intermediate profiles. ConclusionObesity and related health issues frequently cluster with unfavorable environment and SEP at the neighborhood level. The ecosyndemic framework offers a novel approach for identifying high-risk areas and supports targeted, social and place-based interventions.

BackgroundCannabis use is highly prevalent among people who use unregulated drugs. While daily cannabis use has been hypothesized to provide protective effects through substitution or tolerance mechanisms, the relationship between cannabis use frequency and overdose risk remains poorly understood, particularly for infrequent users. MethodsWe conducted a secondary analysis of cross-sectional interview data from people who use unregulated drugs in Vancouver, British Columbia, collected during the fentanyl crisis (November 2019-July 2021; n=657). Binary logistic regression examined associations between self-reported cannabis use frequency (five categories: less than monthly, 1-3 times per month, weekly, more than weekly and daily) and non-fatal overdose in the preceding six months. Daily use served as the reference category. Models adjusted for age, gender, ethnicity, homelessness, mental health, HIV status, incarceration and daily use of alcohol, opioids, fentanyl, cocaine and stimulants. ResultsAmong 657 participants, 95 (14.5%) reported non-fatal overdose in the past six months. In adjusted models with daily cannabis use as the reference, infrequent cannabis use was associated with significantly increased odds of overdose: use 1-3 times per month (aOR=3.17, 95% CI: 1.50-6.69, p=.002) and more than weekly use (aOR=3.13, 95% CI: 1.70-5.76, p<.001) showed approximately three-fold increased odds compared to daily use. Less frequent use showed non-significant trends in the same direction (less than monthly: aOR=1.73, 95% CI: 0.89-3.37, p=.109; weekly: aOR=1.44, 95% CI: 0.59-3.51, p=.421). Sensitivity analysis restricted to participants with daily stimulant or fentanyl use (n=148) revealed even stronger associations. ConclusionsInfrequent cannabis use was associated with substantially increased overdose risk compared to daily use. This frequency-dependent relationship, with infrequent users at highest risk, likely reflects tolerance differences: infrequent users lack tolerance to synergistic cannabis-opioid effects. These findings were completely obscured in preliminary analyses that dichotomized cannabis use as daily versus less-than-daily, demonstrating how analytical choices can mask critical public health insights. Current harm reduction approaches, including cannabis distribution programs, should incorporate frequency-dependent risk communication and develop strategies to protect infrequent users who may be at heightened overdose risk.

BackgroundScalable, non-invasive markers for cognitive-decline risk are limited. Olfactory dysfunction is predictive, and oral dysbiosis is mechanistically linked to neurocognitive pathways. Hence, we tested whether pairing smell and global cognition with salivary microbiome profiling yields a targeted, clinically useful signal. MethodsWe enrolled 113 Memory Center attendees and community controls. Same-day MMSE, UPSIT, and saliva were obtained for 16S rRNA gene sequencing and cytokine measurement. Unsupervised k-means clustering on standardized MMSE-UPSIT defined two groups of participants: CNN (cognitively normal, normosmia) and CIH (cognitively impaired, hyposmia). Ordination and elastic-net models adjusted for age, sex, BMI, and sequencing depth. Functions were inferred with PICRUSt2 and were integrated with taxa via DIABLO. ResultsOverall, the 16S-based microbial community structure was similar between groups, indicating minor compositional shifts. CIH showed enrichment of periodontal anaerobes (Porphyromonas, Treponema and Prevotella), whereas CNN retained nitrate-reducing commensals (e.g. Neisseria subflava, Aggregatibacter aphrophilus). Functional shifts showed mixed consistency with literature, aligning for outer membrane usher proteins and alkyldihydroxy phosphate synthase, but diverging for thiaminase, alpha-glucuronidase, and chemotaxis protein CheX. Most salivary cytokines levels did not differ between groups. ConclusionsThis integrated smell, cognition, and saliva workflow delineates an olfactory- cognitive phenotype linked to a targeted, potentially modifiable salivary dysbiosis, periodontal anaerobes vs nitrate-reducers, rather than diffuse salivary inflammatory elevation. This approach may support non-invasive triage and monitoring along the oral- brain axis, pending independent, longitudinal validation.

BackgroundThe 2024 blood culture bottle shortage brought diagnostic resource allocation to the forefront, reflecting persistent, foundational challenges with low-value testing and empiric treatment approaches under clinical uncertainty. ObjectiveTo determine whether a machine learning approach using electronic medical record data can predict bacteremia more effectively than existing systems and practices to guide diagnostic testing and empiric treatment strategies. MethodsIn a retrospective cohort of 101,812 adult emergency department encounters (2015-2025), we first established an idealized cognitive baseline by evaluating physician and generative AI (GPT-5) application of the professional society-endorsed Fabre framework on a validation subset. We then trained an XGBoost model (Cultryx) on the full cohort to predict bacteremia, benchmarking its performance against real-world clinical heuristics (SIRS, Shapiro Rule). ResultsFor the idealized baseline, physicians applying the Fabre framework achieved 95.7% sensitivity, but GPT-5 automation failed to replicate this standard (71.6% sensitivity). In real-world benchmarking, Cultryx outperformed all clinical heuristics (AUROC 0.810). SIRS lacked specificity (41.2%), driving diagnostic overuse, while the Shapiro Rule lacked sensitivity (70.2%), missing ~30% of bacteremia cases. In contrast, when calibrated to a strict 95% sensitivity target, Cultryx achieved the highest culture volume deferral rate (26.2%, deferring ~ 15,872 bottles with predicted negative results) while maintaining a 98.9% negative predictive value. Cultryxscore, a simplified bedside tool, retained a 20.8% deferral rate. ConclusionsMachine learning provides a superior, data-driven alternative to mainstream clinical heuristics for predicting bacteremia. By maximizing culture deferment without compromising pathogen detection, Cultryx can conserve diagnostic resources, reduce unnecessary empiric antibiotic exposure, and systematically elevate patient safety. SummaryCultryx, a machine learning model for blood culture stewardship, outperforms standard clinical heuristics in predicting bacteremia. This approach could reduce culture utilization by over 26% while preserving pathogen detection, conserving diagnostic resources, reducing unnecessary antibiotic exposure, and elevating patient safety.

BackgroundCandidemia is a rare but life-threatening bloodstream infection that remains difficult to predict using conventional risk stratification approaches, highlighting the need for improved predictive strategies. As a result, empiric antifungal therapy is often delayed even in high-risk patients. MethodsWe developed a deep learning model (PyTorch_EHR) to predict 7-day candidemia risk by using electronic health record data from two large cohorts (Houston Methodist Hospital System [HMHS] and MIMIC-IV), including adult inpatients who underwent at least one blood culture. Model performance was compared with logistic regression (LR), LightGBM, and established intensive care unit candidemia scores. We further implemented a two-step prediction framework integrating candidemia and 30-day mortality risk models to inform empiric antifungal decision-making. ResultsAmong 213,404 and 107,507 patients in the HMHS and MIMIC-IV cohorts, candidemia occurred in fewer than 1% (851 [0.4%] and 634 [0.6%], respectively). PyTorch_EHR outperformed LR, LightGBM, and existing candidemia scores, particularly in terms of area under the precision-recall curve (AUPRC) in HMHS and MIMIC-IV. By integrating 30-day mortality risk, the two-step framework identified an additional 20 and 28 candidemia cases beyond the one-step model, increasing coverage to 61% (121/199) and 46% (68/147) in HMHS and MIMIC-IV, respectively. Many patients identified by the two-step framework had high mortality yet did not receive empiric antifungal therapy (61.1% HMHS; 82.6% MIMIC-IV). ConclusionA two-step deep-learning framework integrating candidemia and mortality risk may support early identification of high-risk patients and facilitate timely empiric antifungal therapy. Prospective studies are warranted to confirm the findings.