Point-of-need mastitis pathogen biosensing in bovine milk: From academic sample preparation novelty to industry prototype field testing.

Bovine mastitis is an inflammation of the mammary gland, and it is the most common infectious disease in dairy cattle. Mastitis reduces milk yield and quality, costing dairy farmers millions of dollars each year. The aim of this study was to develop a point-of-need test for identifying mastitis pathogens that is field portable, cost-effective and can be used with minimal training. Using a proprietary polymer-based milk sample preparation method to rapidly extract pathogen DNA in milk samples, we demonstrated quantitative Polymerase Chain Reaction (qPCR) assays for six common bovine bacterial mastitis pathogens: Staphylococcus aureus, Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Mycoplasma bovis and Escherichia coli. We also implemented this sample preparation method on a prototype point-of-need system in a proof-of-concept field trial to evaluate user experience. Importantly, the protype system enabled a sample-to-result turnaround time of within 70 min to quantitatively detect all six target pathogens. The key advantage of our point-of-need prototype system is being culture-independent yet providing automated milk sample preparation for molecular identification of key mastitis pathogens by non-expert users. Our point-of-need prototype system showed a good correlation to laboratory-based qPCR for target pathogen detection outcomes, thus potentially removing the need for milk samples to be transported off-site for laboratory testing. Above all, we successfully achieved our objective of developing a point-of-need biosensor technology for mastitis and increased its readiness level with industry partners towards technology commercialization.

Experimentally Determined Hansen Solubility Parameters of Biobased and Biodegradable Polyesters.

Hansen solubility parameters (HSP) of 15 commercially relevant biobased and biodegradable polyesters were experimentally determined by applying a novel approach to the classic solubility study method. In this approach, the extent of swelling in polymer films was determined using a simple equation based on the mass difference between swollen and nonswollen film samples to obtain normalized solvent uptake (N). Using N and HSPiP software, highly accurate HSP values were obtained for all 15 polyesters. Qualitative evaluation of the HSP values was conducted by predicting the miscibility of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHB-co-HHx, 7 mol % HHx) and poly(lactic acid) (PLA) with a novel lignin-based plasticizer (ethyl 3-(4-ethoxy-3-methoxyphenyl)propanoate, EP) with a relative energy difference (RED) less than 0.4. Additionally, an HSP-predicted plasticizer (di(2-ethylhexyl) adipate, DA) with a larger RED (>0.7) was used to demonstrate the effects of less-miscible additives. Plasticized samples were analyzed by differential scanning calorimetry and polarized optical microscopy (POM) to determine the Tg depression, with EP showing linear Tg depression up to 50% plasticizer loading, whereas DA shows minimal Tg depression past 10% loading. Further analysis by POM reveals that the DA phase separates from both polymers at loadings as low as 2.5% (PHB-co-HHx, 7 mol % HHx) and 5% (PLA), while the EP phase separates at a much higher loading of 50% (PHB-co-HHx, 7 mol% HHx) and 30% (PLA).

Supervised and unsupervised machine learning approaches for monitoring subvisible particles within an aluminum-salt adjuvanted vaccine formulation.

Suspensions of protein antigens adsorbed to aluminum-salt adjuvants are used in many vaccines and require mixing during vial filling operations to prevent sedimentation. However, the mixing of vaccine formulations may generate undesirable particles that are difficult to detect against the background of suspended adjuvant particles. We simulated the mixing of a suspension containing a protein antigen adsorbed to an aluminum-salt adjuvant using a recirculating peristaltic pump and used flow imaging microscopy to record images of particles within the pumped suspensions. Supervised convolutional neural networks (CNNs) were used to analyze the images and create "fingerprints" of particle morphology distributions, allowing detection of new particles generated during pumping. These results were compared to those obtained from an unsupervised machine learning algorithm relying on variational autoencoders (VAEs) that were also used to detect new particles generated during pumping. Analyses of images conducted by applying both supervised CNNs and VAEs found that rates of generation of new particles were higher in aluminum-salt adjuvant suspensions containing protein antigen than placebo suspensions containing only adjuvant. Finally, front-face fluorescence measurements of the vaccine suspensions indicated changes in solvent exposure of tryptophan residues in the protein that occurred concomitantly with new particle generation during pumping.

Bacterial culture and susceptibility test results for clinical mastitis samples from Australia's subtropical dairy region.

This study aimed to identify the pathogens isolated from the milk of cows with clinical mastitis in the subtropical region of Australia and to determine the antimicrobial susceptibility of these bacteria. Thirty dairy herds in the subtropical dairy region were asked to submit milk samples for the first 5 cases of clinical mastitis each month for 12 mo. Samples underwent aerobic culture, and isolates were identified via MALDI-TOF mass spectrometry. Antimicrobial susceptibility was determined for Escherichia coli, Enterococcus spp., Streptococcus agalactiae, Streptococcus uberis, Streptococcus dysgalactiae, Staphylococcus aureus, and non-aureus staphylococci and mammaliicocci (NASM). Between March 2021 and July 2022, 1,230 milk samples were collected. A positive culture result was recorded for 812 (66%) of the milk samples; from these samples, 909 isolates were obtained, including 49 isolates where no identification was possible. The remaining samples were classified as having no growth (16.8%) or as being contaminated (17.2%). The most common isolates with a MALDI-TOF diagnosis (n = 909) were Strep. uberis (23.6%), followed by the NASM group (15.0%). Farms enrolled in the study were in 3 distinct locations within the subtropical dairy region: North Queensland, Southeast Queensland, and Northern New South Wales. Some variation in isolate prevalence occurred between these 3 locations. We found lower odds of a sample being positive for E. coli in North Queensland (odds ratio [OR]: 0.25; 95% confidence interval [CI]: 0.07-0.87) and higher odds in Southeast Queensland (OR: 4.01; 95% CI: 1.96-8.20) compared with the reference, Northern New South Wales. We further found higher odds of Strep. dysgalactiae in North Queensland (OR: 5.69; 95% CI: 1.85-17.54) and Southeast Queensland compared with Northern New South Wales (OR: 3.99; 95% CI: 1.73-9.22). Although some seasonal patterns were observed, season was not significant for any of the analyzed isolates. Farm-level differences in pathogen profiles were obvious. Overall, clinical mastitis pathogens had low levels of resistance to the antimicrobials tested. This research demonstrates that Strep. uberis and the NASM bacterial group are the most common pathogens causing clinical mastitis in the subtropical dairy region. It highlights the importance of understanding pathogenic causes of mastitis at the farm and regional level for targeted control and therapy.

School-based Mental Health Supports During COVID-19: School Professional Perspectives.

Objective: The present study explored the ways school professionals adapted school-based mental health supports and services for remote delivery during the COVID-19 pandemic. Method: We surveyed 81 school professionals (e.g., counselors, psychologists, social workers) and conducted in-depth interviews with a subsample of professionals (n=14) to explore their perceptions and experiences of supporting youth with mental health concerns and suicide-related risk during the fall and winter of the 2020-2021 school year. Results: Commonly endorsed school-based mental health interventions (e.g., counseling services, checking in), ways of communicating (phone, email), and individuals delivering supports and services to students with suicide-related risk (e.g., counselors, teachers) were identified based on school professional survey responses. Qualitative findings point to facilitators (e.g., specific platforms for connecting with students and families) and barriers (e.g., limited communication) to successful service delivery during COVID-19. Conclusion: Findings highlight the creative ways school support professionals adapted to provide school-based mental health supports. Implications for remote school-based mental health services during and following the pandemic are discussed.

Bacterial culture and antimicrobial susceptibility results from bovine milk samples submitted to four veterinary diagnostic laboratories in Australia from 2015 to 2019.

A 5-year retrospective study was conducted to describe the mastitis-causing organisms isolated from bovine milk samples submitted to four veterinary diagnostic laboratories in Australia. The aim of this study was to identify temporal, geographical, and seasonal patterns of occurrence for the organisms and report the in vitro susceptibility of the most common mastitis-causing pathogens. In total, 22,102 milk samples were submitted between 2015 and 2019. The results were reported as positive growth for at least one significant organism (n = 11,407; 51.6%), no growth (n = 5,782; 26.2%), and mixed/contaminated growth (n = 4,913; 22.2%). Culture results for no growth, gram-negative bacteria, and eukaryotic organisms were combined for each region, and they were accounted for between 23 and 46% of submissions. These results represent a subset of mastitis cases for which the antibiotic treatment may not be warranted. A total of 11,907 isolates were cultured from 11,407 milk samples. The most common isolated organisms were Streptococcus uberis [41.3%; 95% confidence interval (CI): 40.4-42.1%] and Staphylococcus aureus (23.6%; 95% CI: 22.8-24.3%). For S. uberis and S. aureus, there was an association between a positive culture result and the dairy region. All regions except for the Sub-tropical Dairy region were more likely to culture S. uberis compared to the reference, Dairy NSW (P < 0.001). Similarly, for S. aureus, a positive culture result was more likely in all other dairy regions compared to Dairy NSW (P < 0.001). The LISA cluster analysis identified differences between High-High (hotspot) postcodes for S. aureus and S. uberis throughout all the analyzed dairy regions. These results highlight the need for further investigations into specific risk factors, such as environmental factors and herd-level predictors, which may have influenced the observed regional variations. Common mastitis-causing pathogens showed overall good susceptibility to a range of antimicrobials used in the treatment of mastitis. On-going surveillance of mastitis-causing pathogens and their antimicrobial susceptibilities will facilitate targeted mastitis control and treatment programs.

Deep learning image analysis models pretrained on daily objects are useful for the preliminary characterization of particulate pharmaceutical samples.

Visible and subvisible particles are a quality attribute in sterile pharmaceutical samples. A common method for characterizing and quantifying pharmaceutical samples containing particulates is imaging many individual particles using high-throughput instrumentation and analyzing the populations data. The analysis includes conventional metrics such as the particle size distribution but can be more sophisticated by interpreting other visual/morphological features. To avoid the hurdles of building new image analysis models capable of extracting such relevant features from scratch, we propose using well-established pretrained deep learning image analysis models such as EfficientNet. We demonstrate that such models are useful as a prescreening tool for high-level characterization of biopharmaceutical particle image data. We show that although these models are originally trained for completely different tasks (such as the classification of daily objects in the ImageNet database), the visual feature vectors extracted by such models can be useful for studying different types of subvisible particles. This applicability is illustrated through multiple case studies: (i) particle risk assessment in prefilled syringe formulations containing different particle types such as silicone oil, (ii) method comparability with the example of accelerated forced degradation, and (iii) excipient influence on particle morphology with the example of Polysorbate 80 (PS80). As examples of agnostic applicability of pretrained models, we also elucidate the application to two high-throughput microscopy methods: microflow and background membrane imaging. We show that different particle populations with different morphological and visual features can be identified in different samples by leveraging out-of-the-box pretrained models to analyze images from each sample.

Antibodies Adsorbed to the Air-Water Interface Form Soft Glasses.

When monoclonal antibodies are exposed to an air-water interface, they form aggregates, which negatively impacts their performance. Until now, the detection and characterization of interfacial aggregation have been difficult. Here, we exploit the mechanical response imparted by interfacial adsorption by measuring the interfacial shear rheology of a model antibody, anti-streptavidin immunoglobulin-1 (AS-IgG1), at the air-water interface. Strong viscoelastic layers of AS-IgG1 form when the protein is adsorbed from the bulk solution. Creep experiments correlate the compliance of the interfacial protein layer with the subphase solution pH and bulk concentration. These, along with oscillatory strain amplitude and frequency sweeps, show that the viscoelastic behavior of the adsorbed layers is that of a soft glass with interfacial shear moduli on the order of 10-3 Pa m. Shifting the creep compliance curves under different applied stresses forms master curves consistent with stress-time superposition of soft interfacial glasses. The interfacial rheology results are discussed in the context of the interface-mediated aggregation of AS-IgG1.

Profiling Risk Factors for Household and Community Spatiotemporal Clusters of Q Fever Notifications in Queensland between 2002 and 2017.

Q fever, caused by the bacterium Coxiella burnetii, is an important zoonotic disease worldwide. Australia has one of the highest reported incidences and seroprevalence of Q fever, and communities in the state of Queensland are at highest risk of exposure. Despite Australia's Q fever vaccination programs, the number of reported Q fever cases has remained stable for the last few years. The extent to which Q fever notifications cluster in circumscribed communities is not well understood. This study aimed to retrospectively explore and identify the spatiotemporal variation in Q fever household and community clusters in Queensland reported during 2002 to 2017, and quantify potential within cluster drivers. We used Q fever notification data held in the Queensland Notifiable Conditions System to explore the geographical clustering patterns of Q fever incidence, and identified and estimated community Q fever spatiotemporal clusters using SatScan, Boston, MA, USA. The association between Q fever household and community clusters, and demographic and socioeconomic characteristics was explored using the chi-squared statistical test and logistic regression analysis. From the total 2175 Q fever notifications included in our analysis, we found 356 Q fever hotspots at a mesh-block level. We identified that 8.2% of Q fever notifications belonged to a spatiotemporal cluster. Within the spatiotemporal Q fever clusters, we found 44 (61%) representing household clusters and 20 (27.8%) were statistically significant with an average cluster size of 3 km radius. Our multivariable model shows statistical differences between cases belonging to clusters in comparison with cases outside clusters based on the type of reported exposure. In conclusion, our results demonstrate that clusters of Q fever notifications are temporally stable and geographically circumscribed, indicating a persistent common exposure. Furthermore, within individuals in household and community clusters, abattoir exposure (a traditional occupational exposure) was rarely reported by individuals.

A Rapid, Small-Volume Approach to Evaluate Protein Aggregation at Air-Water Interfaces.

Non-native protein aggregation is a common concern for biopharmaceuticals. A given protein may aggregate through a variety of mechanisms that depend on solution and physico-chemical stress conditions. A thorough evaluation of aggregation behavior for a protein under all conditions of interest is necessary to ensure drug safety and efficacy. This work introduces a rapid, small-volume approach to evaluate protein aggregation propensity upon exposure to air-water interfaces (AWI). A microtensiometer apparatus is used to aerate a small volume of a protein solution with microbubbles for short periods of time (≤10 s). Sub-visible particles that form are captured and analyzed using backgrounded membrane imaging. This allows one to capture all particles in the solution while being sample sparing. The surface-mediated aggregation of two model monoclonal antibodies (MAbs) and a globular protein (aCgn) was tested as a function of pH and temperature. Temperature had a negligible effect under the rapid interface turnover time scales with this technique. Electrostatic protein-protein interactions, mediated by pH changes, were more influential for particle formation via AWI. Nonionic surfactants substantially reduced particle formation for all MAb solutions, but not aCgn. The results are contrasted with expectations when exposing samples to much larger air-water interfacial stress.

The improving state of Q fever surveillance. A review of Queensland notifications, 2003-2017.

Q fever is a notifiable zoonotic disease in Australia, caused by infection with Coxiella burnetii. This study has reviewed 2,838 Q fever notifications reported in Queensland between 2003 and 2017 presenting descriptive analyses, with counts, rates, and proportions. For this study period, Queensland accounted for 43% of the Australian national Q fever notifications. Enhanced surveillance follow-up of Q fever cases through Queensland Public Health Units was implemented in 2012, which improved the data collected for occupational risk exposures and animal contacts. For 2013-2017, forty-nine percent (377/774) of cases with an identifiable occupational group would be considered high risk for Q fever. The most common identifiable occupational group was agricultural/farming (31%). For the same period, at-risk environmental exposures were identified in 82% (961/1,170) of notifications; at-risk animal-related exposures were identified in 52% (612/1,170) of notifications; abattoir exposure was identified in 7% of notifications. This study has shown that the improved follow-up of Q fever cases since 2012 has been effective in the identification of possible exposure pathways for Q fever transmission. This improved surveillance has highlighted the need for further education and heightened awareness of Q fever risk for all people living in Queensland, not just those in previously-considered high risk occupations.

Unravelling animal exposure profiles of human Q fever cases in Queensland, Australia, using natural language processing.

Q fever, caused by the zoonotic bacterium Coxiella burnetii, is a globally distributed emerging infectious disease. Livestock are the most important zoonotic transmission sources, yet infection in people without livestock exposure is common. Identifying potential exposure pathways is necessary to design effective interventions and aid outbreak prevention. We used natural language processing and graphical network methods to provide insights into how Q fever notifications are associated with variation in patient occupations or lifestyles. Using an 18-year time-series of Q fever notifications in Queensland, Australia, we used topic models to test whether compositions of patient answers to follow-up exposure questionnaires varied between demographic groups or across geographical areas. To determine heterogeneity in possible zoonotic exposures, we explored patterns of livestock and game animal co-exposures using Markov Random Fields models. Finally, to identify possible correlates of Q fever case severity, we modelled patient probabilities of being hospitalized as a function of particular exposures. Different demographic groups consistently reported distinct sets of exposure terms and were concentrated in different areas of the state, suggesting the presence of multiple transmission pathways. Macropod exposure was commonly reported among Q fever cases, even when exposure to cattle, sheep or goats was absent. Males, older patients and those that reported macropod exposure were more likely to be hospitalized due to Q fever infection. Our study indicates that follow-up surveillance combined with text modelling is useful for unravelling exposure pathways in the battle to reduce Q fever incidence and associated morbidity.

Kinetics and Competing Mechanisms of Antibody Aggregation via Bulk- and Surface-Mediated Pathways.

Non-native protein aggregation is a long-standing obstacle in the biopharmaceutical industry. Proteins can aggregate through different mechanisms, depending on the solution and stress conditions. Aggregation in bulk solution has been extensively studied in a mechanistic context and is known to be temperature dependent. Conversely, aggregation at interfaces has been commonly observed for liquid formulations but is less understood mechanistically. This work evaluates the combined effects of temperature and compression/dilation of air-water interfaces on aggregation rates and particle formation for anti-streptavidin immunoglobulin gamma-1. Aggregation rates are quantified via size-exclusion chromatography, dynamic light scattering, and microflow imaging as a function of temperature and extent of air-liquid interface compressions. Competition exists between bulk- and surface-mediated aggregation mechanisms. Each has a largely different temperature dependence that leads to a crossover between the dominant aggregation mechanisms as the sample temperature changes. Surface-mediated aggregation rates are pH dependent and correlate with electrostatic protein-protein interactions but do not mirror the pH dependence of bulk aggregation rates that instead follow trends for conformational stability. Mechanistic insights were informed by quiescent incubation of solutions before and after interface compressions. Detailed mechanistic conclusions require direct dynamic observation at the interface. Microbubble tensiometry is introduced as a promising tool for such measurements.

Validation of an indirect immunofluorescence assay (IFA) for the detection of IgG antibodies against Coxiella burnetii in bovine serum.

There is limited knowledge of the true prevalence and distribution of coxiellosis in dairy and beef cattle populations in Australia. For this to occur, apparent prevalence estimates need to be reliably adjusted, accounting for diagnostic sensitivity (DSe) and diagnostic specificity (DSp) of the test used. However, there are few tests available with known diagnostic specifications suitable to inform screening and surveillance activities in the Australian context. We initially modified and optimised a human indirect immunofluorescence assay (IFA) test for the detection of IgG antibodies against phase I and/or phase II Coxiella burnetii in bovine sera and determined an optimal screening dilution cut-off to be 1:160. Direct comparison of the modified IFA with the commercial IDEXX enzyme-linked immunosorbent assay (ELISA) kit (Q Fever Ab Test IDEXX Laboratories, United States of America) was performed by testing 458 serum samples from four distinct cattle populations across the east coast of Australia and New Zealand. Cross classified test results were then analysed using Bayesian latent class modelling, to validate the tests in the absence of a gold standard reference test. Results from this analysis indicate that the IFA, at a 1:160 serum dilution, has an estimated DSe of 73.6% (95% Credible Interval (CrI) 61.1, 85.9) and DSp of 98.2% (95% CrI 95.1, 99.7). The commercial IDEXX ELISA kit was found to have a higher DSe of 87.9% (95% CrI 73.9, 96.4) and similar DSp of 97.7% (95% CrI 93.2, 99.7). Evaluation of the diagnostic performance of the IFA and ELISA methods, specifically for use in cattle will enable more accurate interpretation of prevalence estimates of C. burnetii exposure to be reported for cattle in Australia and other countries.