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Abstract Background E. coli O83 (Colinfant Newborn) is a Gram-negative (G-) probiotic bacterium used in the clinic. When administered orally, it reduces allergic sensitisation but not allergic asthma. Intranasal administration offers a non-invasive and convenient delivery method. This route bypasses the gastrointestinal tract and provides direct access to the airways, which are the target of asthma prevention. G- bacteria such as E. coli O83 release outer membrane vesicles (OMVs) to communicate with the environment. Here we investigate whether intranasally administered E. coli O83 OMVs (EcO83-OMVs) can reduce allergic airway inflammation in mice. Methods EcO83-OMVs were isolated by ultracentrifugation and characterised their number, morphology (shape and size), composition (proteins and lipopolysaccharide; LPS), recognition by innate receptors (using transfected HEK293 cells) and immunomodulatory potential (in naïve splenocytes and bone marrow-derived dendritic cells; BMDCs). Their allergy-preventive effect was investigated in a mouse model of ovalbumin-induced allergic airway inflammation. Results EcO83-OMVs are spherical nanoparticles with a size of about 110 nm. They contain LPS and protein cargo. We identified a total of 1120 proteins, 136 of which were enriched in OMVs compared to parent bacteria. Proteins from the flagellum dominated. OMVs activated the pattern recognition receptors TLR2/4/5 as well as NOD1 and NOD2. EcO83-OMVs induced the production of pro- and anti-inflammatory cytokines in splenocytes and BMDCs. Intranasal administration of EcO83-OMVs inhibited airway hyperresponsiveness, and decreased airway eosinophilia, Th2 cytokine production and mucus secretion. Conclusions We demonstrate for the first time that intranasally administered OMVs from probiotic G- bacteria have an anti-allergic effect. Our study highlights the advantages of OMVs as a safe platform for the prophylactic treatment of allergy. Graphical Abstract Video Abstract

Objectives Many adolescents and young adults with emerging mood disorders do not achieve substantial improvements in education, employment, or social function after receiving standard youth mental health care. We have developed a new model of care referred to as ‘highly personalised and measurement-based care’ (HP&MBC). HP&MBC involves repeated assessment of multidimensional domains of morbidity to enable continuous and personalised clinical decision-making. Although measurement-based care is common in medical disease management, it is not a standard practice in mental health. This clinical effectiveness trial tests whether HP&MBC, supported by continuous digital feedback, delivers better functional improvements than standard care and digital support.Method and analysis This controlled implementation trial is a PROBE study (Prospective, Randomised, Open, Blinded End-point) that comprises a multisite 24-month, assessor-blinded, follow-up study of 1500 individuals aged 15–25 years who present for mental health treatment. Eligible participants will be individually randomised (1:1) to 12 months of HP&MBC or standardised clinical care. The primary outcome measure is social and occupational functioning 12 months after trial entry, assessed by the Social and Occupational Functioning Assessment Scale. Clinical and social outcomes for all participants will be monitored for a further 12 months after cessation of active care.Ethics and dissemination This clinical trial has been reviewed and approved by the Human Research Ethics Committee of the Sydney Local Health District (HREC Approval Number: X22-0042 & 2022/ETH00725, Protocol ID: BMC-YMH-003-2018, protocol version: V.3, 03/08/2022). Research findings will be disseminated through peer-reviewed journals, presentations at scientific conferences, and to user and advocacy groups. Participant data will be deidentified.Trial registration number ACTRN12622000882729.

Concentrations of SARS-CoV-2 RNA in wastewater settled solids from publicly owned treatment works (POTWs) historically correlated strongly with laboratory confirmed incident COVID-19 case data. With the increased availability of at-home antigen tests since late 2021 and early 2022, laboratory test availability and test seeking behavior has decreased. In the United States, the results from at-home antigen tests are not typically reportable to public health agencies and thus are not counted in case reports. As a result, the number of reported laboratory-confirmed incident COVID-19 cases has decreased dramatically, even during times of increased test positivity rates and wastewater concentrations of SARS-CoV-2 RNA. Herein, we tested whether the correlative relationship between wastewater concentrations of SARS-CoV-2 RNA and reported laboratory-confirmed COVID-19 incidence rate has changed since 1 May 2022, a point in time immediately before the onset of the BA.2/BA.5 surge, the first surge to begin after at-home antigen test availability was high in the region. We used daily data from three POTWs in the Greater San Francisco Bay Area of California, USA for the analysis. We found that although there is a significant positive association between wastewater measurements and incident rate data collected after 1 May 2022, the parameters describing the relationship are different than those describing the relationship between the data collected prior to 1 May 2022. If laboratory test seeking or availability continues to change, the relationship between wastewater and reported case data will continue to change. Our results suggest, assuming SARS-CoV-2 RNA shedding remains relatively stable among those infected with the virus as different variants emerge, that wastewater concentrations of SARS-CoV-2 RNA can be used to estimate COVID-19 cases as they would have been during the time when laboratory testing availability and test seeking behavior were at a high (here, before 1 May 2022) using the historical relationship between SARS-CoV-2 RNA and COVID-19 case data.

BackgroundPredicting patient's Length of Stay (LOS) before total joint replacement (TJR) surgery is vital for hospitals to optimally manage costs and resources. Many hospitals including in rural areas use publicly available models such as National Surgical Quality Improvement Program (NSQIP) calculator which, unfortunately, performs suboptimally when predicting LOS for TJR procedures.ObjectiveThe objective of this research was to develop a Machine Learning (ML) model to predict LOS for TJR procedures performed at a Perioperative Surgical Home implemented rural community hospital for better accuracy and interpretation than the NSQIP calculator.MethodsA total of 158 TJR patients were collected and analyzed from a rural community hospital located in Montana. A random forest (RF) model was used to predict patient's LOS. For interpretation, permuted feature importance and partial dependence plot methods were used to identify the important variables and their relationship with the LOS.ResultsThe root mean square error for the RF model (0.7) was lower than the NSQIP calculator (1.21). The five most important variables for predicting LOS were BMI, Duke Activity Status-Index, diabetes, patient's household income, and patient's age.ConclusionThis pilot study is the first of its kind to develop an ML model to predict LOS for TJR procedures that were performed at a small-scale rural community hospital. This pilot study contributes an approach for rural hospitals, making them more independent by developing their own predictions instead of relying on public models.

Although early-stage affective disorders are associated with both cognitive dysfunction and sleep-wake disruptions, relationships between these factors have not been specifically examined in young adults. Sleep and circadian rhythm disturbances in those with affective disorders are considerably heterogeneous, and may not relate to cognitive dysfunction in a simple linear fashion. This study aimed to characterise profiles of sleep and circadian disturbance in young people with affective disorders and examine associations between these profiles and cognitive performance. Actigraphy monitoring was completed in 152 young people (16-30 years; 66% female) with primary diagnoses of affective disorders, and 69 healthy controls (18-30 years; 57% female). Patients also underwent detailed neuropsychological assessment. Actigraphy data were processed to estimate both sleep and circadian parameters. Overall neuropsychological performance in patients was poor on tasks relating to mental flexibility and visual memory. Two hierarchical cluster analyses identified three distinct patient groups based on sleep variables and three based on circadian variables. Sleep clusters included a 'long sleep' cluster, a 'disrupted sleep' cluster, and a 'delayed and disrupted sleep' cluster. Circadian clusters included a 'strong circadian' cluster, a 'weak circadian' cluster, and a 'delayed circadian' cluster. Medication use differed between clusters. The 'long sleep' cluster displayed significantly worse visual memory performance compared to the 'disrupted sleep' cluster. No other cognitive functions differed between clusters. These results highlight the heterogeneity of sleep and circadian profiles in young people with affective disorders, and provide preliminary evidence in support of a relationship between sleep and visual memory, which may be mediated by use of antipsychotic medication. These findings have implications for the personalisation of treatments and improvement of functioning in young adults early in the course of affective illness.

Considering the marked changes in sleep and circadian rhythms across the lifespan, age may contribute to the heterogeneity in sleep-wake profiles linked to mood disorders. This study aimed to investigate the contributions of age and depression severity to sleep-wake disturbances. The Hamilton Depression Rating Scale (HDRS) was administered to assess current symptoms severity in 238 persons with a history of a mood disorder between 12 and 90 years of age (y.o.). Actigraphy was recorded over five to 22 days. Regression analyses and analyses of variance [age (12-19 y.o., 20-39 y.o., 40-59 y.o., and ≥ 60 y.o.) by depression severity (HDRS< and ≥ 8)] were conducted. The 12-19 y.o. and 20-39 y.o. groups had a delayed sleep schedule and acrophase compared to all other groups. The ≥ 60 y.o. group had a lower rhythmicity and amplitude (p ≤ .006) than the 12-19 y.o. group (p ≤ .046). Participants with a HDRS ≥ 8 spent longer time in bed, had later sleep offset times and had lower circadian rhythmicity than those with a HDRS